diff --git a/PATSTAT/patstat_analyses.ipynb b/PATSTAT/patstat_analyses.ipynb new file mode 100644 index 0000000..b62e474 --- /dev/null +++ b/PATSTAT/patstat_analyses.ipynb @@ -0,0 +1,121 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": " \n " + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import janitor\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from matplotlib.ticker import MaxNLocator\n", + "import math\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "import plotly.offline as pyo\n", + "from plotly.subplots import make_subplots\n", + "import plotly.graph_objects as go\n", + "pyo.init_notebook_mode()\n", + "\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"plotly_mimetype+notebook\"\n", + "\n", + "import country_converter as coco\n", + "cc = coco.CountryConverter()\n", + "\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "os.makedirs('plot_html/PPT_plots',exist_ok=True)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "agg_ds = 'aggregated_data'" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "cn = pd.read_csv(f\"{agg_ds}/01_counts_cty_yr_CN.csv\", header=None)\n", + "eur = pd.read_csv(f\"{agg_ds}/01_counts_cty_yr_eu_27_GB.csv\", header=None)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " 0 1 2\n0 2011 AT 2395\n1 2011 BE 646\n2 2011 BG 330\n3 2011 CY 1256\n4 2011 CZ 2246\n.. ... .. ...\n7 2018 CN 3242424\n8 2019 CN 3547407\n9 2020 CN 4367505\n10 2021 CN 4012952\n11 2022 CN 2613602\n\n[359 rows x 3 columns]", + "text/html": "
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" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.concat([eur,cn])" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/PATSTAT/patstat_analysis_pipeline.ipynb b/PATSTAT/patstat_analysis_pipeline.ipynb index 552d87e..9fcd864 100644 --- a/PATSTAT/patstat_analysis_pipeline.ipynb +++ b/PATSTAT/patstat_analysis_pipeline.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -14,18 +14,68 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from matplotlib.ticker import MaxNLocator\n", + "import math\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/html": " \n " + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import janitor\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from matplotlib.ticker import MaxNLocator\n", "import math\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "import plotly.offline as pyo\n", + "from plotly.subplots import make_subplots\n", + "import plotly.graph_objects as go\n", + "pyo.init_notebook_mode()\n", + "\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"plotly_mimetype+notebook\"\n", + "\n", + "import country_converter as coco\n", + "cc = coco.CountryConverter()\n", + "\n", + "\n", "%matplotlib inline" - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "outputs": [], "source": [ "outdir=\"EU_CH_scope\"\n", "\n", - "appln = pd.read_csv(f\"{outdir}/tls_201_scope.csv\")\n", + "appln = pd.read_csv(f\"{outdir}/tls_201_scope.csv\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [], + "source": [ "\n", "appln_title = pd.read_csv(f\"{outdir}/tls_202_scope.csv\")\n", "\n", @@ -36,7 +86,7 @@ "\n", "appln_cpc = pd.read_csv(f\"{outdir}/tls_224_scope.csv\")\n", "\n", - "cpc_def = pd. read_csv(\"CPC_data/cpc_defs.csv\", low_memory=False)" + "cpc_def = pd.read_csv(\"CPC_data/cpc_defs.csv\", low_memory=False)" ], "metadata": { "collapsed": false @@ -44,19 +94,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 106, "outputs": [ { "data": { - "text/plain": "65136" + "text/plain": " person_id person_name \n27780 38752029 BOTH HENDRIK \\\n42972 47186505 Zhang, Richard S. \n39368 45225635 Roche Diagnostics Operations, Inc. \n17349 13164592 LI, QINGHUA \n38005 44432440 YANG, CHENGGUAN \n17354 13164808 JAVAHERIAN, KASHI \n48330 48886066 KRAUSE FALK \n25002 24615058 BUTLER, HANS \n70951 54647336 Schmieder, Wolfgang \n8906 11399816 De Boer, Dirk Kornelis Gerhardus \n\n person_name_orig_lg person_address person_ctry_code \n27780 BOTH HENDRIK NaN NL \\\n42972 Zhang, Richard S. 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358 rows × 20 columns

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" + "text/plain": " cpc_id cpc_name \n0 A HUMAN NECESSITIES \\\n1 A01 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n2 A01B SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... \n3 A01B1/00 Hand tools (edge trimmers for lawns A01G3/06 ... \n4 A01B1/02 Spades; Shovels {(hand-operated dredgers E02F3... \n... ... ... \n260486 Y10T483/1873 Indexing matrix \n260487 Y10T483/1882 Rotary disc \n260488 Y10T483/1891 Chain or belt \n260489 Y10T483/19 Miscellaneous \n260490 NaN NaN \n\n section class subclass group main_group cpc_version \n0 A NaN NaN NaN NaN 2023 \\\n1 A 1.0 NaN NaN NaN 2023 \n2 A 1.0 B NaN NaN 2023 \n3 A 1.0 B 1.0 0.0 2023 \n4 A 1.0 B 1.0 2.0 2023 \n... ... ... ... ... ... ... \n260486 Y 10.0 T 483.0 1873.0 2023 \n260487 Y 10.0 T 483.0 1882.0 2023 \n260488 Y 10.0 T 483.0 1891.0 2023 \n260489 Y 10.0 T 483.0 19.0 2023 \n260490 NaN NaN NaN NaN NaN 2022 \n\n version https://git-lfs.github.com/spec/v1 \n0 NaN \\\n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n260486 NaN \n260487 NaN \n260488 NaN \n260489 NaN \n260490 oid sha256:f138d6bdf2939ba576b96b633d81366123b... \n\n cpc_taxonomy \n0 [('A', 'HUMAN NECESSITIES')] \\\n1 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n2 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n3 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n4 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n... ... \n260486 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260487 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260488 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260489 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260490 [] \n\n cpc_fullname \n0 HUMAN NECESSITIES \\\n1 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n2 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n3 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n4 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n... ... \n260486 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260487 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260488 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260489 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260490 NaN \n\n tax_level_0 \n0 HUMAN NECESSITIES \\\n1 HUMAN NECESSITIES \n2 HUMAN NECESSITIES \n3 HUMAN NECESSITIES \n4 HUMAN NECESSITIES \n... ... \n260486 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260487 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260488 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260489 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260490 NaN \n\n tax_level_1 \n0 NaN \\\n1 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n2 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n3 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n4 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n... ... \n260486 TECHNICAL SUBJECTS COVERED BY FORMER USPC \n260487 TECHNICAL SUBJECTS COVERED BY FORMER USPC \n260488 TECHNICAL SUBJECTS COVERED BY FORMER USPC \n260489 TECHNICAL SUBJECTS COVERED BY FORMER USPC \n260490 NaN \n\n tax_level_2 \n0 NaN \\\n1 NaN \n2 SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... \n3 SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... \n4 SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... \n... ... \n260486 TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... \n260487 TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... \n260488 TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... \n260489 TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... \n260490 NaN \n\n tax_level_3 tax_level_4 \n0 NaN NaN \\\n1 NaN NaN \n2 NaN NaN \n3 Hand tools (edge trimmers for lawns A01G3/06 ... NaN \n4 Spades; Shovels {(hand-operated dredgers E02F3... NaN \n... ... ... \n260486 Tool transfer to or from matrix Indexing matrix \n260487 Tool transfer to or from matrix Rotary disc \n260488 Tool transfer to or from matrix Chain or belt \n260489 Miscellaneous NaN \n260490 NaN NaN \n\n tax_level_5 tax_level_6 tax_level_7 data_scope \n0 NaN NaN NaN False \n1 NaN NaN NaN False \n2 NaN NaN NaN False \n3 NaN NaN NaN False \n4 NaN NaN NaN False \n... ... ... ... ... \n260486 NaN NaN NaN False \n260487 NaN NaN NaN False \n260488 NaN NaN NaN False \n260489 NaN NaN NaN False \n260490 NaN NaN NaN False \n\n[260491 rows x 20 columns]", + "text/html": "
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cpc_idcpc_namesectionclasssubclassgroupmain_groupcpc_versionversion https://git-lfs.github.com/spec/v1cpc_taxonomycpc_fullnametax_level_0tax_level_1tax_level_2tax_level_3tax_level_4tax_level_5tax_level_6tax_level_7data_scope
0AHUMAN NECESSITIESANaNNaNNaNNaN2023NaN[('A', 'HUMAN NECESSITIES')]HUMAN NECESSITIESHUMAN NECESSITIESNaNNaNNaNNaNNaNNaNNaNFalse
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260486Y10T483/1873Indexing matrixY10.0T483.01873.02023NaN[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...TECHNICAL SUBJECTS COVERED BY FORMER USPCTECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...Tool transfer to or from matrixIndexing matrixNaNNaNNaNFalse
260487Y10T483/1882Rotary discY10.0T483.01882.02023NaN[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...TECHNICAL SUBJECTS COVERED BY FORMER USPCTECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...Tool transfer to or from matrixRotary discNaNNaNNaNFalse
260488Y10T483/1891Chain or beltY10.0T483.01891.02023NaN[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...TECHNICAL SUBJECTS COVERED BY FORMER USPCTECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...Tool transfer to or from matrixChain or beltNaNNaNNaNFalse
260489Y10T483/19MiscellaneousY10.0T483.019.02023NaN[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...TECHNICAL SUBJECTS COVERED BY FORMER USPCTECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...MiscellaneousNaNNaNNaNNaNFalse
260490NaNNaNNaNNaNNaNNaNNaN2022oid sha256:f138d6bdf2939ba576b96b633d81366123b...[]NaNNaNNaNNaNNaNNaNNaNNaNNaNFalse
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260491 rows × 20 columns

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" }, - "execution_count": 14, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "cpc_def[cpc_def[\"data_scope\"]==True]" + "cpc_def" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 28, + "outputs": [ + { + "data": { + "text/plain": " appln_id cpc_class_symbol\n461523 550229597 C08G 18/6674\n511948 569882441 C08K 5/06\n67828 414454432 G06F 21/105\n83905 418994277 H04B 7/0452\n393107 530331540 H04N 19/70\n406153 534165470 C07K2317/52\n253473 490601228 D06M2200/50\n256305 491586869 G01G 11/003\n279783 497310121 A61K 39/3955\n432151 541221711 G01N 15/1012", + "text/html": "
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appln_idcpc_class_symbol
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511948569882441C08K 5/06
67828414454432G06F 21/105
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432151541221711G01N 15/1012
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" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appln_cpc.sample(10)" ], "metadata": { "collapsed": false @@ -89,8 +161,7 @@ "outputs": [ { "data": { - "text/plain": " appln_id appln_auth appln_nr appln_kind appln_filing_date \n33934 497577215 BR 112014017433 A 2013-01-15 \\\n2981 375177905 TW 100128243 A 2011-08-08 \n34857 500398671 US 201815918757 A 2018-03-12 \n14330 425909090 US 201313948663 A 2013-07-23 \n12337 421949639 US 201313799810 A 2013-03-13 \n... ... ... ... ... ... \n20853 451232832 US 201314892356 A 2013-06-05 \n57178 558607779 WO 2020082459 W 2020-03-31 \n60907 571924211 WO 2022025191 W 2022-04-27 \n36638 505223478 CA 3027451 A 2017-06-14 \n33120 496062856 US 201615739023 A 2016-06-21 \n\n appln_filing_year appln_nr_original ipr_type receiving_office \n33934 2013 112014017433 PI \\\n2981 2011 100128243 PI \n34857 2018 15918757 PI \n14330 2013 13948663 PI \n12337 2013 13799810 PI \n... ... ... ... ... \n20853 2013 14892356 PI \n57178 2020 CN2020/082459 PI CN \n60907 2022 EP2022/025191 PI EP \n36638 2017 3027451 PI \n33120 2016 15739023 PI \n\n internat_appln_id ... earliest_publn_date earliest_publn_year \n33934 379982555 ... 2017-06-13 2017 \\\n2981 0 ... 2012-04-01 2012 \n34857 329543408 ... 2018-09-27 2018 \n14330 0 ... 2015-01-29 2015 \n12337 0 ... 2014-09-18 2014 \n... ... ... ... ... \n20853 424456242 ... 2016-04-14 2016 \n57178 0 ... 2021-10-07 2021 \n60907 0 ... 2022-11-24 2022 \n36638 479992612 ... 2017-12-21 2017 \n33120 473240582 ... 2018-06-21 2018 \n\n earliest_pat_publn_id granted docdb_family_id inpadoc_family_id \n33934 490635945 N 47605490 379982555 \\\n2981 380050378 N 44677979 336552941 \n34857 500398672 Y 43858368 273565445 \n14330 425909091 Y 51167733 419869995 \n12337 421949640 Y 50241294 416000492 \n... ... ... ... ... \n20853 451232833 Y 52007415 424456242 \n57178 558607780 N 77927568 558607779 \n60907 583339295 N 81756758 571924211 \n36638 505223479 N 58241906 476382802 \n33120 496062857 Y 53496595 442016492 \n\n docdb_family_size nb_citing_docdb_fam nb_applicants nb_inventors \n33934 9 6 1 4 \n2981 5 6 1 3 \n34857 12 17 1 3 \n14330 3 19 1 4 \n12337 4 19 1 2 \n... ... ... ... ... \n20853 7 14 1 3 \n57178 3 0 2 3 \n60907 2 0 1 1 \n36638 5 3 1 3 \n33120 3 4 2 2 \n\n[100 rows x 26 columns]", - "text/html": "
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12337421949639US201313799810A2013-03-13201313799810PI0...2014-09-182014421949640Y5024129441600049241912
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100 rows × 26 columns

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appln_idappln_title_lgappln_title
46635531117386enFRACTURING OPERATIONS PUMP FLEET BALANCE CONTR...
44552526116621enSHEETLIKE COMPOSITE, IN PARTICULAR FOR THE PRO...
45476528352131enNATURALLY SWEET ENHANCER COMPOSITION
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33233496310125enMETHOD AND DEVICE FOR CONNECTING TO ACCESS POI...
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61597574732234enADJUSTMENT MECHANISM FOR CIRCUIT BREAKER AND C...
54568551189734enMETHODS FOR PDCCH MONITORING, USER EQUIPMENT, ...
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" + "text/plain": "65136" }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "appln_title.sample(100)" + "len(appln)" ], "metadata": { "collapsed": false @@ -127,30 +197,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "outputs": [], - "source": [], + "source": [ + "# Number of collaborative applications\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 39, + "outputs": [], + "source": [ + "record_col = \"appln_id\"" + ], "metadata": { "collapsed": false } }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "outputs": [ { "data": { - "text/plain": " appln_id appln_auth appln_nr appln_kind appln_filing_date \n0 330225325 EP 11150195 A 2011-01-05 \\\n1 330225397 EP 11150231 A 2011-01-05 \n2 330322632 EP 11150485 A 2011-01-10 \n3 330326785 EP 11150605 A 2011-01-11 \n4 330350961 EP 11150683 A 2011-01-12 \n\n appln_filing_year appln_nr_original ipr_type receiving_office \n0 2011 11150195 PI \\\n1 2011 11150231 PI \n2 2011 11150485 PI \n3 2011 11150605 PI \n4 2011 11150683 PI \n\n internat_appln_id ... earliest_pat_publn_id granted docdb_family_id \n0 0 ... 335277427 Y 43754737 \\\n1 0 ... 335277736 Y 43619902 \n2 0 ... 364719889 Y 43991052 \n3 0 ... 335277720 N 43023665 \n4 0 ... 364923578 N 43881056 \n\n inpadoc_family_id docdb_family_size nb_citing_docdb_fam nb_applicants \n0 330225325 4 16 1 \\\n1 330225397 6 56 1 \n2 330322632 2 5 1 \n3 328518903 6 9 1 \n4 330350961 7 13 2 \n\n nb_inventors appln_title_lg \n0 1 en \\\n1 9 en \n2 2 en \n3 3 en \n4 5 en \n\n appln_title \n0 Beverage preparation machine \n1 Screwdriving tool having a driving tool with a... \n2 Method and system for recommending contextual ... \n3 Apparatus and method for continuous casting of... \n4 A method and an apparatus for treating at leas... \n\n[5 rows x 28 columns]", - "text/html": "
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appln_idappln_authappln_nrappln_kindappln_filing_dateappln_filing_yearappln_nr_originalipr_typereceiving_officeinternat_appln_id...earliest_pat_publn_idgranteddocdb_family_idinpadoc_family_iddocdb_family_sizenb_citing_docdb_famnb_applicantsnb_inventorsappln_title_lgappln_title
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2330322632EP11150485A2011-01-10201111150485PI0...364719889Y439910523303226322512enMethod and system for recommending contextual ...
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" + "text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'" }, - "execution_count": 23, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "appln_data = appln.merge(appln_title, on=\"appln_id\")\n", - "appln_data.head()" + "os.getcwd()" ], "metadata": { "collapsed": false @@ -158,19 +239,19 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 20, "outputs": [ { "data": { - "text/plain": "array(['EP', 'WO', 'LU', 'FI', 'NO', 'FR', 'GB', 'KR', 'ES', 'US', 'CA',\n 'DO', 'EC', 'DE', 'UY', 'IL', 'SV', 'PL', 'TR', 'CO', 'CR', 'TW',\n 'MA', 'PE', 'SG', 'CU', 'BE', 'DK', 'AR', 'AP', 'HR', 'MX', 'BR',\n 'EA', 'RU', 'AU', 'MC', 'HU', 'PT', 'NL', 'HN', 'AT', 'RO', 'SM',\n 'CH', 'SI', 'IS', 'CZ', 'HK', 'MD', 'JP', 'CN', 'RS', 'GT', 'UA',\n 'CL', 'SK', 'LT', 'PH', 'MY', 'IN', 'VN', 'TN', 'CY', 'GE', 'ZA',\n 'SE', 'ME', 'JO', 'NI', 'SA'], dtype=object)" + "text/plain": "count 65136.000000\nmean 2016.312546\nstd 3.189059\nmin 2011.000000\n25% 2013.000000\n50% 2017.000000\n75% 2019.000000\nmax 2023.000000\nName: appln_filing_year, dtype: float64" }, - "execution_count": 24, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "appln_data[\"appln_auth\"].unique()" + "appln[\"appln_filing_year\"].describe()" ], "metadata": { "collapsed": false @@ -178,20 +259,31 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 61, "outputs": [ { "data": { - "text/plain": " person_id appln_id applt_seq_nr invt_seq_nr\n0 1 413601768 1 0\n1 21 332015605 1 0\n2 21 333490084 1 0\n3 21 335903805 1 0\n4 76 352908776 1 0\n... ... ... ... ...\n1025446 88836321 577982223 1 0\n1025447 88836333 583342135 0 4\n1025448 88836333 583342207 0 3\n1025449 88836333 585957705 0 5\n1025450 88836337 579601496 0 1\n\n[1025451 rows x 4 columns]", - "text/html": "
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1025451 rows × 4 columns

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" + "text/plain": " appln_id appln_auth appln_kind filing_year nb_applicants \n445321 902305835 EP A 2017 0 \\\n480488 515101583 CN A 2018 2 \n307745 458113107 CN A 2016 1 \n352956 449856958 EP A 2016 1 \n452231 510599290 CN A 2018 1 \n... ... ... ... ... ... \n424840 496723291 CN A 2017 1 \n84346 378450888 GB A 2012 1 \n736784 576111604 CN A 2021 1 \n617397 528722596 CN A 2019 1 \n65667 411664922 DE A 2012 1 \n\n nb_inventors \n445321 0 \n480488 5 \n307745 3 \n352956 4 \n452231 3 \n... ... \n424840 1 \n84346 0 \n736784 3 \n617397 1 \n65667 1 \n\n[100 rows x 6 columns]", + "text/html": "
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" }, - "execution_count": 25, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "appln_pers" + "outdir=\"first_round\"\n", + "\n", + "appln_f = pd.read_csv(f\"{outdir}/first-filings-raw.csv\", header=None,\n", + " names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors'])\n", + "\n", + "appln_f[\"filing_year\"] = appln_f[\"filing_year\"].astype(int)\n", + "\n", + "appln_f = appln_f[appln_f[\"filing_year\"]>2010]\n", + "\n", + "appln_f[\"appln_auth\"] = appln_f[\"appln_auth\"].str.replace(\"MA\",\"CN\")\n", + "\n", + "appln_f.sample(100)" ], "metadata": { "collapsed": false @@ -199,32 +291,1195 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 40, "outputs": [ { "data": { - "text/plain": " person_id person_name person_name_orig_lg \n0 1 Nokia Corporation Nokia Corporation \\\n1 128 Nokia Siemens Networks Oy Nokia Siemens Networks Oy \n2 5217785 Nokia Corporation Nokia Corporation \n3 5217811 Nokia Corporation Nokia Corporation \n4 5232170 Nokia Siemens Networks Oy Nokia Siemens Networks Oy \n... ... ... ... \n354633 88836234 WONG, Chun Lok WONG, Chun Lok \n354634 88836257 XIAONING YE XIAONING YE \n354635 88836321 ZAI LAB (US) LLC ZAI LAB (US) LLC \n354636 88836333 ZHANG, Haocheng 张皓程 \n354637 88836337 ZHANG, Yangjun ZHANG, Yangjun \n\n person_address person_ctry_code nuts nuts_level \n0 Keilalahdentie 4,02150 Espoo FI FI1B1 3 \\\n1 Karaportti 3,02610 Espoo FI FI1B1 3 \n2 Espoo FI FI 0 \n3 NaN FI FI 0 \n4 Espoo FI FI 0 \n... ... ... ... ... \n354633 NaN US NaN 9 \n354634 Portland, Oregon US US NaN 9 \n354635 NaN US NaN 9 \n354636 NaN US NaN 9 \n354637 NaN US NaN 9 \n\n doc_std_name_id doc_std_name psn_id \n0 1 NOKIA CORP 23782051 \\\n1 112 NOKIA SIEMENS NETWORKS OY 23782129 \n2 1 NOKIA CORP 23782051 \n3 1 NOKIA CORP 23782051 \n4 112 NOKIA SIEMENS NETWORKS OY 23782129 \n... ... ... ... \n354633 30867225 WONG CHUN LOK 188836234 \n354634 8004293 XIAONING YE 188836257 \n354635 39363494 ZAI LAB US LLC 188836321 \n354636 7682590 ZHANG HAOCHENG 188836333 \n354637 2112344 ZHANG YANGJUN 188836337 \n\n psn_name psn_level psn_sector han_id han_name \n0 NOKIA CORPORATION 2 COMPANY 2125445 NOKIA CORP \\\n1 NOKIA NETWORKS 2 COMPANY 2125445 NOKIA CORP \n2 NOKIA CORPORATION 2 COMPANY 2125445 NOKIA CORP \n3 NOKIA CORPORATION 2 COMPANY 2125445 NOKIA CORP \n4 NOKIA NETWORKS 2 COMPANY 2125445 NOKIA CORP \n... ... ... ... ... ... \n354633 WONG, Chun Lok 0 UNKNOWN 188836234 WONG, Chun Lok \n354634 XIAONING YE 0 UNKNOWN 188836257 XIAONING YE \n354635 ZAI LAB (US) LLC 0 UNKNOWN 188836321 ZAI LAB (US) LLC \n354636 ZHANG, Haocheng 0 UNKNOWN 188836333 ZHANG, Haocheng \n354637 ZHANG, Yangjun 0 UNKNOWN 188836337 ZHANG, Yangjun \n\n han_harmonized psn_sector_primary \n0 2 COMPANY \n1 2 COMPANY \n2 2 COMPANY \n3 2 COMPANY \n4 2 COMPANY \n... ... ... \n354633 0 UNKNOWN \n354634 0 UNKNOWN \n354635 0 UNKNOWN \n354636 0 UNKNOWN \n354637 0 UNKNOWN \n\n[354638 rows x 17 columns]", - "text/html": "
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......................................................
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1.0 + ], + "categoryorder": "total ascending", + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "range": [ + 0, + 150000 + ] + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.0, + 1.0 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside" + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0.0, + 0.375 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "zerolinecolor": "grey", + "tickformat": ".0%", + "range": [ + -1, + 1.5 + ] + }, + "annotations": [ + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "Co-patents per year", + "x": 0.5, + "xanchor": "center", + "xref": "paper", + "y": 1.0, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "Relative growth of co-patents", + "x": 0.5, + "xanchor": "center", + "xref": "paper", + "y": 0.375, + "yanchor": "bottom", + "yref": "paper" + } + ], + "barmode": "relative", + "font": { + "family": "Montserrat" + } + }, + "config": { + "displayModeBar": false, + "responsive": true, + "plotlyServerURL": "https://plotly.com" + } + }, + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = (appln_f.groupby(['filing_year'])[record_col].nunique(dropna=False)\n", + " .reset_index()\n", + " .rename(columns={0:record_col}))\n", + "data[record_col+\"_relative_growth\"] = data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True)[record_col][0]\n", + "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", + "\n", + "data = data.sort_values(by =[\"filing_year\"], ascending=[True])\n", + "data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n", + "\n", + "year_output = px.line(data,x=\"filing_year\", y=record_col, markers=True, text=record_col)\n", + "year_output.update_traces(textposition='top center',textfont_size=12)\n", + "year_output.update_traces(hovertemplate='Year:%{x:d}
Number of co-publications:%{y:d}')\n", + "\n", + "year_rel_output = px.line(data,x=\"filing_year\", y=record_col+\"_relative_growth\", markers=True, text=record_col+\"_relative_growth\")\n", + "year_rel_output.update_traces(textposition='top center',textfont_size=12, texttemplate='%{y:.0%}')\n", + "year_rel_output.update_traces(hovertemplate='Year:%{x:d}
Rel.growth in co-publications:%{y:.0%}')\n", + "\n", + "figsuper = make_subplots(rows=2, cols=1, subplot_titles=[\"Co-patents per year\",\"Relative growth of co-patents\"],\n", + " specs=[\n", + " [{\"type\": \"xy\"}],\n", + " [{\"type\": \"xy\"}]\n", + " ])\n", + "\n", + "for trace in list(year_output.select_traces()):\n", + " figsuper.add_trace(trace,\n", + " row=1, col=1\n", + " )\n", + "\n", + "for trace in list(year_rel_output.select_traces()):\n", + " figsuper.add_trace(trace,\n", + " row=2, col=1\n", + " )\n", + "\n", + "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", + "figsuper.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "figsuper.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", + "figsuper['layout']['yaxis2'].update(zerolinecolor='grey',tickformat=\".0%\")\n", + "# for i in[\"xaxis\",\"xaxis2\"]:\n", + "# figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n", + "#\n", + "figsuper['layout'][\"yaxis\"][\"range\"] = [0,150000]\n", + "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,1.5]\n", + "#\n", + "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n", + "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n", + "\n", + "figsuper.show(config= dict(displayModeBar = False, responsive = True))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 63, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "EP not found in ISO2\n" + ] }, { "data": { - "text/plain": " person_id person_name person_name_orig_lg \n0 1 Nokia Corporation Nokia Corporation \\\n1 128 Nokia Siemens Networks Oy Nokia Siemens Networks Oy \n2 5217785 Nokia Corporation Nokia Corporation \n3 5217811 Nokia Corporation Nokia Corporation \n4 5232170 Nokia Siemens Networks Oy Nokia Siemens Networks Oy \n... ... ... ... \n354633 88836234 WONG, Chun Lok WONG, Chun Lok \n354634 88836257 XIAONING YE XIAONING YE \n354635 88836321 ZAI LAB (US) LLC ZAI LAB (US) LLC \n354636 88836333 ZHANG, Haocheng 张皓程 \n354637 88836337 ZHANG, Yangjun ZHANG, Yangjun \n\n person_address person_ctry_code nuts nuts_level \n0 Keilalahdentie 4,02150 Espoo FI FI1B1 3 \\\n1 Karaportti 3,02610 Espoo FI FI1B1 3 \n2 Espoo FI FI 0 \n3 NaN FI FI 0 \n4 Espoo FI FI 0 \n... ... ... ... ... \n354633 NaN US NaN 9 \n354634 Portland, Oregon US US NaN 9 \n354635 NaN US NaN 9 \n354636 NaN US NaN 9 \n354637 NaN US NaN 9 \n\n doc_std_name_id doc_std_name psn_id \n0 1 NOKIA CORP 23782051 \\\n1 112 NOKIA SIEMENS NETWORKS OY 23782129 \n2 1 NOKIA CORP 23782051 \n3 1 NOKIA CORP 23782051 \n4 112 NOKIA SIEMENS NETWORKS OY 23782129 \n... ... ... ... \n354633 30867225 WONG CHUN LOK 188836234 \n354634 8004293 XIAONING YE 188836257 \n354635 39363494 ZAI LAB US LLC 188836321 \n354636 7682590 ZHANG HAOCHENG 188836333 \n354637 2112344 ZHANG YANGJUN 188836337 \n\n psn_name psn_level psn_sector han_id han_name \n0 NOKIA CORPORATION 2 COMPANY 2125445 NOKIA CORP \\\n1 NOKIA NETWORKS 2 COMPANY 2125445 NOKIA CORP \n2 NOKIA CORPORATION 2 COMPANY 2125445 NOKIA CORP \n3 NOKIA CORPORATION 2 COMPANY 2125445 NOKIA CORP \n4 NOKIA NETWORKS 2 COMPANY 2125445 NOKIA CORP \n... ... ... ... ... ... \n354633 WONG, Chun Lok 0 UNKNOWN 188836234 WONG, Chun Lok \n354634 XIAONING YE 0 UNKNOWN 188836257 XIAONING YE \n354635 ZAI LAB (US) LLC 0 UNKNOWN 188836321 ZAI LAB (US) LLC \n354636 ZHANG, Haocheng 0 UNKNOWN 188836333 ZHANG, Haocheng \n354637 ZHANG, Yangjun 0 UNKNOWN 188836337 ZHANG, Yangjun \n\n han_harmonized psn_sector_primary \n0 2 COMPANY \n1 2 COMPANY \n2 2 COMPANY \n3 2 COMPANY \n4 2 COMPANY \n... ... ... \n354633 0 UNKNOWN \n354634 0 UNKNOWN \n354635 0 UNKNOWN \n354636 0 UNKNOWN \n354637 0 UNKNOWN \n\n[354638 rows x 17 columns]", - "text/html": "
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" }, - "execution_count": 26, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pers_sector_primary = pers.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n", - " lambda x: pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})\n", - "persn = pers.merge(pers_sector_primary, on='han_id')\n", - "persn" + "data = (appln_f.groupby(['filing_year',\"appln_auth\"])[record_col]\n", + " .nunique(dropna=False).unstack()\n", + " .fillna(0)\n", + " .stack()\n", + " .reset_index()\n", + " .rename(columns={0:record_col}))\n", + "\n", + "data[\"appln_auth\"].str.replace(\"MO\",\"CN\")\n", + "\n", + "data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset=\"appln_auth\"),\n", + " on=[\"appln_auth\"], suffixes=[None,\"_relative_growth\"])\n", + "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", + "data = data.sort_values(by =[\"appln_auth\",\"filing_year\"], ascending=[True,True])\n", + "data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n", + "data[\"Country\"] = cc.pandas_convert(series=data[\"appln_auth\"], to='name_short')\n", + "data[\"Country\"] = data[\"Country\"].str.replace(\"not found\",\"European Patent Office\")\n", + "data" ], "metadata": { "collapsed": false @@ -232,29 +1487,8825 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 68, "outputs": [ { "data": { - "text/plain": " han_id psn_sector_primary\n0 32 COMPANY\n1 54 COMPANY\n2 83 COMPANY\n3 200 COMPANY\n4 264 GOV NON-PROFIT UNIVERSITY\n... ... ...\n335519 188836234 UNKNOWN\n335520 188836257 UNKNOWN\n335521 188836321 UNKNOWN\n335522 188836333 UNKNOWN\n335523 188836337 UNKNOWN\n\n[335524 rows x 2 columns]", - "text/html": "
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" }, - "execution_count": 27, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "pers_sector_primary" + "fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n", + " y=record_col+\"_relative_growth\",\n", + " x='filing_year'\n", + " ,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", + " markers=True,\n", + " labels={\n", + " record_col+\"_relative_growth\": 'Relative growth of co-patents (%)',\n", + " },\n", + " title=\"Yearly output of co-patents by patent authority\",hover_name= \"Country\")\n", + "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", + "fig.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "fig.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "fig.update_layout({'template':\"plotly\"})\n", + "fig.update_yaxes(title='')\n", + "fig.update_xaxes(title='')\n", + "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", + "# fig.show(config= dict(displayModeBar = False, responsive = True))\n", + "\n", + "figsuper_ppt = go.Figure(fig)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + "s=16\n", + "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + "figsuper_ppt.show()" ], "metadata": { "collapsed": false @@ -262,7 +10313,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "outputs": [], "source": [], "metadata": { @@ -271,11 +10322,50 @@ }, { "cell_type": "code", - "execution_count": 28, - "outputs": [], + "execution_count": 142, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "EP not found in ISO2\n" + ] + }, + { + "data": { + "text/plain": " appln_id appln_auth person_id invt_seq_nr applt_seq_nr \n987983 449313178 FR 53981953 1 0 \\\n896220 438489934 FR 52986688 4 0 \n1703159 484622346 EP 57708518 1 0 \n20051 471352386 DE 5235181 0 1 \n151664 337008716 FR 42812566 1 0 \n... ... ... ... ... ... \n818875 436949973 FR 51872306 2 0 \n195230 523590443 DE 16958864 2 0 \n129138 578626678 DE 6193358 0 1 \n276045 449666385 DE 41815193 2 0 \n24195 530683500 DE 5235181 0 1 \n\n person_name person_ctry_code psn_sector han_id \n987983 BREARD MATTHIEU FR NaN 153981953 \\\n896220 ENGBLOM CHRISTER FR NaN 152986688 \n1703159 VAN INGELGEM, Werner BE NaN 157708518 \n20051 Robert Bosch GmbH DE COMPANY 2456791 \n151664 BRESOUS LUC FR NaN 142812566 \n... ... ... ... ... \n818875 BORDERIEUX, LOIC FR NaN 151872306 \n195230 WEYDANZ, WOLFGANG DE INDIVIDUAL 116958864 \n129138 Clariant International Ltd. CH COMPANY 524840 \n276045 WELTER, MARTIN DE INDIVIDUAL 141815193 \n24195 Robert Bosch GmbH DE COMPANY 2456791 \n\n han_name person_ctry_code_fullname \n987983 BREARD MATTHIEU France \\\n896220 ENGBLOM CHRISTER France \n1703159 VAN INGELGEM, Werner Belgium \n20051 ROBERT BOSCH GMBH Germany \n151664 BRESOUS LUC France \n... ... ... \n818875 BORDERIEUX, LOIC France \n195230 WEYDANZ, WOLFGANG Germany \n129138 CLARIANT INT LTD Switzerland \n276045 WELTER, MARTIN Germany \n24195 ROBERT BOSCH GMBH Germany \n\n appln_auth_fullname \n987983 France \n896220 France \n1703159 European Patent Office \n20051 Germany \n151664 France \n... ... \n818875 France \n195230 Germany \n129138 Germany \n276045 Germany \n24195 Germany \n\n[100 rows x 12 columns]", + "text/html": "
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appln_idappln_authperson_idinvt_seq_nrapplt_seq_nrperson_nameperson_ctry_codepsn_sectorhan_idhan_nameperson_ctry_code_fullnameappln_auth_fullname
987983449313178FR5398195310BREARD MATTHIEUFRNaN153981953BREARD MATTHIEUFranceFrance
896220438489934FR5298668840ENGBLOM CHRISTERFRNaN152986688ENGBLOM CHRISTERFranceFrance
1703159484622346EP5770851810VAN INGELGEM, WernerBENaN157708518VAN INGELGEM, WernerBelgiumEuropean Patent Office
20051471352386DE523518101Robert Bosch GmbHDECOMPANY2456791ROBERT BOSCH GMBHGermanyGermany
151664337008716FR4281256610BRESOUS LUCFRNaN142812566BRESOUS LUCFranceFrance
.......................................
818875436949973FR5187230620BORDERIEUX, LOICFRNaN151872306BORDERIEUX, LOICFranceFrance
195230523590443DE1695886420WEYDANZ, WOLFGANGDEINDIVIDUAL116958864WEYDANZ, WOLFGANGGermanyGermany
129138578626678DE619335801Clariant International Ltd.CHCOMPANY524840CLARIANT INT LTDSwitzerlandGermany
276045449666385DE4181519320WELTER, MARTINDEINDIVIDUAL141815193WELTER, MARTINGermanyGermany
24195530683500DE523518101Robert Bosch GmbHDECOMPANY2456791ROBERT BOSCH GMBHGermanyGermany
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100 rows × 12 columns

\n
" + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "appln_merge = appln.merge(appln_title, on=\"appln_id\")#.merge(appln_pers,on=\"appln_id\")\n", - "appln_merge.to_excel(\"appln_data.xlsx\", index=False)" + "appln_pers_f = pd.read_csv(f\"{outdir}/first-filings-with-persons-raw.csv\", header=None,\n", + " names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n", + "\n", + "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n", + "\n", + "appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")\n", + "\n", + "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n", + "# tls_801.head()\n", + "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n", + " (tls_801.ctry_code == 'CN')|\n", + " (tls_801.ctry_code == 'NO')|\n", + " (tls_801.ctry_code == 'CH')|\n", + " (tls_801.ctry_code == 'UK'))]\n", + "scope_countries = scope_df[\"ctry_code\"].unique()\n", + "\n", + "appln_pers_f = appln_pers_f[appln_pers_f[\"person_ctry_code\"].isin(scope_countries)]\n", + "\n", + "for c in [\"person_ctry_code\",\"appln_auth\"]:\n", + " appln_pers_f[c+\"_fullname\"] = cc.pandas_convert(series=appln_pers_f[c], to='name_short')\n", + "\n", + "appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n", + "\n", + "appln_pers_f.sample(100)" ], "metadata": { "collapsed": false @@ -283,11 +10373,2898 @@ }, { "cell_type": "code", - "execution_count": 29, - "outputs": [], + "execution_count": 143, + "outputs": [ + { + "data": { + "text/plain": "283091" + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "person_merge = appln_pers.merge(pers,on=\"person_id\")\n", - "person_merge.to_excel(\"person_data.xlsx\", index=False)" + "appln_pers_f[\"appln_id\"].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 146, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "appln_id_percent=%{x}
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"bgcolor": "rgb(17,17,17)", + "landcolor": "rgb(17,17,17)", + "subunitcolor": "#506784", + "showland": true, + "showlakes": true, + "lakecolor": "rgb(17,17,17)" + }, + "title": { + "x": 0.05 + }, + "updatemenudefaults": { + "bgcolor": "#506784", + "borderwidth": 0 + }, + "sliderdefaults": { + "bgcolor": "#C8D4E3", + "borderwidth": 1, + "bordercolor": "rgb(17,17,17)", + "tickwidth": 0 + }, + "mapbox": { + "style": "dark" + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0.0, + 1.0 + ], + "title": { + "text": "appln_id_percent" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0.0, + 1.0 + ], + "title": { + "text": "appln_auth_fullname" + } + }, + "legend": { + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "barmode": "relative" + }, + "config": { + "plotlyServerURL": "https://plotly.com" + } + }, + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for c in [\"person_ctry_code\",\"appln_auth\"]:\n", + " pers_sub = appln_pers_f\n", + " if c == \"person_ctry_code\":\n", + " for role in [\"invt\",\"applt\"]:\n", + " pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n", + "\n", + " data = pers_sub.groupby(c+\"_fullname\",as_index=False)[record_col].nunique()\n", + " data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n", + " fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\")\n", + " fig.show()\n", + " else:\n", + " data = pers_sub.groupby(c+\"_fullname\",as_index=False)[record_col].nunique()\n", + " data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n", + " fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\")\n", + " fig.show()" ], "metadata": { "collapsed": false @@ -295,17 +13272,3164 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 126, "outputs": [ { "data": { - "text/plain": "array(['FI', 'NL', 'FR', 'CH', 'US', 'DE', 'DK', 'AT', 'SE', 'BE', 'CN',\n 'IT', 'LU', 'IE', 'SI', 'HK', 'MO', 'CZ', 'ES', 'NO', 'PL', 'HU',\n 'CY', 'SK', 'PT', 'EE', 'MT', 'GR', 'RO', 'BG', 'LT', 'HR', 'LV'],\n dtype=object)" + "text/plain": " appln_auth appln_id appln_id_percent\n0 AT 6016 0.009362\n1 BE 1772 0.002758\n2 BG 229 0.000356\n3 CH 1926 0.002997\n4 CN 304654 0.474115\n5 CZ 1373 0.002137\n6 DE 124035 0.193028\n7 DK 1480 0.002303\n8 EE 46 0.000072\n9 EP 54082 0.084165\n10 ES 8484 0.013203\n11 FI 4719 0.007344\n12 FR 57391 0.089314\n13 GB 51069 0.079476\n14 GR 515 0.000801\n15 HR 135 0.000210\n16 HU 807 0.001256\n17 IE 70 0.000109\n18 IT 5535 0.008614\n19 LT 58 0.000090\n20 LU 1054 0.001640\n21 LV 60 0.000093\n22 MA 363 0.000565\n23 NL 7377 0.011480\n24 PL 1854 0.002885\n25 PT 385 0.000599\n26 RO 235 0.000366\n27 SE 6207 0.009660\n28 SI 430 0.000669\n29 SK 213 0.000331", + "text/html": "
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appln_authappln_idappln_id_percent
0AT60160.009362
1BE17720.002758
2BG2290.000356
3CH19260.002997
4CN3046540.474115
5CZ13730.002137
6DE1240350.193028
7DK14800.002303
8EE460.000072
9EP540820.084165
10ES84840.013203
11FI47190.007344
12FR573910.089314
13GB510690.079476
14GR5150.000801
15HR1350.000210
16HU8070.001256
17IE700.000109
18IT55350.008614
19LT580.000090
20LU10540.001640
21LV600.000093
22MA3630.000565
23NL73770.011480
24PL18540.002885
25PT3850.000599
26RO2350.000366
27SE62070.009660
28SI4300.000669
29SK2130.000331
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" }, - "execution_count": 30, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], + "source": [ + "data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# percent of publications related to\n", + "# -authority country\n", + "# -inventor country\n", + "# -applicant country" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# top 25\n", + "# - inventor\n", + "# - applicant\n", + "# in china, EU, EUextended" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 111, + "outputs": [ + { + "data": { + "text/plain": " person_id psn_sector han_id \n269495 48715290 COMPANY 148715290 \\\n225401 45096499 GOV NON-PROFIT 986555 \n426484 58455492 NaN 158455492 \n50805 15325724 INDIVIDUAL 115325724 \n112503 28064269 NaN 128064269 \n... ... ... ... \n464708 59491689 NaN 159491689 \n169950 42286280 INDIVIDUAL 4099527 \n485679 60214508 NaN 160214508 \n542210 62188978 NaN 162188978 \n752136 81407527 NaN 181407527 \n\n han_name \n269495 TECHFLOW MARINE LIMITED \n225401 FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC... \n426484 DING ZHIJIAO \n50805 FOURNIER, THIERRY \n112503 ADRIAN WALLER \n... ... \n464708 LIU YANZHI \n169950 RIEMANN STEFAN \n485679 WAN BO \n542210 CHU WEI \n752136 GARCIA MONSONIS, Javier \n\n[100 rows x 4 columns]", + "text/html": "
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person_idpsn_sectorhan_idhan_name
26949548715290COMPANY148715290TECHFLOW MARINE LIMITED
22540145096499GOV NON-PROFIT986555FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC...
42648458455492NaN158455492DING ZHIJIAO
5080515325724INDIVIDUAL115325724FOURNIER, THIERRY
11250328064269NaN128064269ADRIAN WALLER
...............
46470859491689NaN159491689LIU YANZHI
16995042286280INDIVIDUAL4099527RIEMANN STEFAN
48567960214508NaN160214508WAN BO
54221062188978NaN162188978CHU WEI
75213681407527NaN181407527GARCIA MONSONIS, Javier
\n

100 rows × 4 columns

\n
" + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n", + "appln_pers_haninfo.sample(100)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 95, + "outputs": [ + { + "data": { + "text/plain": " appln_id appln_auth appln_kind filing_year nb_applicants \n209158 404772731 CN A 2012 1 \\\n924078 507793803 CN A 2018 1 \n399887 425594860 FR A 2014 1 \n521812 443742521 CN A 2015 1 \n1279727 524830291 CN A 2019 1 \n... ... ... ... ... ... \n967174 496304374 GB A 2018 1 \n602289 492364744 SE A 2016 1 \n229323 410162871 FR A 2013 1 \n1051611 507623754 FR A 2018 1 \n1050112 500224940 CN A 2018 1 \n\n nb_inventors ipc_class_symbol ipc_class_level cpc_id \n209158 7 C07C 41/16 A C07C41/16 \\\n924078 12 B29C 35/02 A B29C35/02 \n399887 2 A61P 7/04 A A61P7/04 \n521812 1 H04W 48/16 A H04W48/16 \n1279727 1 H01L 51/56 A H01L51/56 \n... ... ... ... ... \n967174 2 A61F 13/53 A A61F13/53 \n602289 3 C08L 5/08 A C08L5/08 \n229323 5 H05K 13/04 A H05K13/04 \n1051611 4 F02C 7/32 A F02C7/32 \n1050112 1 A61P 31/00 A A61P31/00 \n\n ipc_class ipc_classname \n209158 C-Chemistry; metallurgy Chemistry; metallurgy \n924078 B-Operations; transporting Operations; transporting \n399887 A-Human necessities Human necessities \n521812 H-Electricity Electricity \n1279727 H-Electricity Electricity \n... ... ... \n967174 A-Human necessities Human necessities \n602289 C-Chemistry; metallurgy Chemistry; metallurgy \n229323 H-Electricity Electricity \n1051611 F-Engineering Engineering \n1050112 A-Human necessities Human necessities \n\n[100 rows x 11 columns]", + "text/html": "
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100 rows × 11 columns

\n
" + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appln_ipc_f = pd.read_csv(f\"{outdir}/first-filings-with-IPC-raw.csv\", header=None,\n", + " names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors',\n", + " 'ipc_class_symbol','ipc_class_level'])\n", + "\n", + "appln_ipc_f = appln_ipc_f[appln_ipc_f[\"filing_year\"]>2010]\n", + "appln_ipc_f[\"cpc_id\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\")\n", + "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\").str[0]\n", + "appln_ipc_f[\"ipc_classname\"] = appln_ipc_f[\"ipc_class\"].replace({\"A\":\"Human necessities\",\n", + " \"B\":\"Performing operations; transporting\",\n", + " \"B\":\"Operations; transporting\",\n", + " \"C\":\"Chemistry; metallurgy\",\n", + " \"D\":\"Textiles; paper\",\n", + " \"E\":\"Fixed constructions\",\n", + " \"F\":\"Mechanical engineering; lightning; heating; weapons; blasting\",\n", + " \"F\":\"Engineering\",\n", + " \"G\":\"Physics\",\n", + " \"H\":\"Electricity\"})\n", + "\n", + "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class\"]+\"-\"+appln_ipc_f[\"ipc_classname\"]\n", + "appln_ipc_f.sample(100)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 96, + "outputs": [], + "source": [ + "group = 'ipc_class'\n", + "data = appln_ipc_f.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n", + "\n", + "fig = px.bar(data.sort_values(by=group), x=record_col, y=group, color=group,barmode='relative',text_auto=True,\n", + " labels={\n", + " record_col: 'Number of co-publications',\n", + " group: \"\",\n", + " },\n", + " title=\"Distribution of Domains\", template='plotly')\n", + "fig.update_layout(showlegend=False, xaxis_tickformat='d',font_family=\"Montserrat\")\n", + "fig.update_traces(hovertemplate='%{x:d}')\n", + "fig.update_traces(textfont_size=12, textangle=0, textposition=\"outside\", cliponaxis=True)\n", + "fig.add_shape(\n", + " # Rectangle with reference to the plot\n", + " type=\"rect\",\n", + " xref=\"paper\",\n", + " yref=\"paper\",\n", + " x0=0,\n", + " y0=0,\n", + " x1=1.0,\n", + " y1=1.0,\n", + " line=dict(\n", + " color=\"black\",\n", + " width=0.5,\n", + " )\n", + " )\n", + "fig.update_layout(yaxis={'categoryorder':'total ascending'})\n", + "fig.update_yaxes(\n", + " showgrid=True,\n", + " ticks=\"outside\")\n", + "fig.update_xaxes(\n", + " showgrid=True,\n", + " ticks=\"outside\")\n", + "dom_distr = go.Figure(fig)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 97, + "outputs": [ + { + "data": { + "text/plain": " filing_year ipc_class appln_id filing_year_relative_growth \n0 2011 A-Human necessities 6063.0 2011 \\\n1 2012 A-Human necessities 5804.0 2011 \n2 2013 A-Human necessities 6301.0 2011 \n3 2014 A-Human necessities 6415.0 2011 \n4 2015 A-Human necessities 7455.0 2011 \n.. ... ... ... ... \n91 2018 H-Electricity 19820.0 2011 \n92 2019 H-Electricity 21883.0 2011 \n93 2020 H-Electricity 23436.0 2011 \n94 2021 H-Electricity 13101.0 2011 \n95 2022 H-Electricity 10.0 2011 \n\n appln_id_relative_growth appln_id_cumsum \n0 0.000000 6063.0 \n1 -0.042718 11867.0 \n2 0.039254 18168.0 \n3 0.058057 24583.0 \n4 0.229589 32038.0 \n.. ... ... \n91 0.607070 121682.0 \n92 0.774345 143565.0 \n93 0.900268 167001.0 \n94 0.062272 180102.0 \n95 -0.999189 180112.0 \n\n[96 rows x 6 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
filing_yearipc_classappln_idfiling_year_relative_growthappln_id_relative_growthappln_id_cumsum
02011A-Human necessities6063.020110.0000006063.0
12012A-Human necessities5804.02011-0.04271811867.0
22013A-Human necessities6301.020110.03925418168.0
32014A-Human necessities6415.020110.05805724583.0
42015A-Human necessities7455.020110.22958932038.0
.....................
912018H-Electricity19820.020110.607070121682.0
922019H-Electricity21883.020110.774345143565.0
932020H-Electricity23436.020110.900268167001.0
942021H-Electricity13101.020110.062272180102.0
952022H-Electricity10.02011-0.999189180112.0
\n

96 rows × 6 columns

\n
" + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = (appln_ipc_f.groupby(['filing_year','ipc_class'])[record_col].nunique(dropna=False).unstack()\n", + " .fillna(0)\n", + " .stack()\n", + " .reset_index()\n", + " .rename(columns={0:record_col}))\n", + "data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset='ipc_class'),\n", + " on='ipc_class', suffixes=[None,\"_relative_growth\"])\n", + "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", + "\n", + "data = data.sort_values(by =[\"ipc_class\",\"filing_year\"], ascending=[True,True])\n", + "data[record_col+\"_cumsum\"] = (data.groupby('ipc_class',as_index=False)[record_col].cumsum())\n", + "data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 98, + "outputs": [], + "source": [ + "group = ['filing_year','ipc_class']\n", + "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col,x=group[0], color=group[-1], markers=True, labels={\n", + " record_col: 'Number of co-publications',\n", + " group[-1]: \"Domain\",\n", + " },\n", + " title=\"Yearly output of co-publications\", template='plotly')\n", + "fig.update_traces(hovertemplate='%{y:d}')\n", + "fig.update_layout(hovermode='x unified')\n", + "fig.add_shape(\n", + " # Rectangle with reference to the plot\n", + " type=\"rect\",\n", + " xref=\"paper\",\n", + " yref=\"paper\",\n", + " x0=0,\n", + " y0=0,\n", + " x1=1.0,\n", + " y1=1.0,\n", + " line=dict(\n", + " color=\"black\",\n", + " width=0.5,\n", + " )\n", + " )\n", + "fig.update_yaxes(\n", + " showgrid=True,\n", + " ticks=\"outside\")\n", + "fig.update_xaxes(\n", + " showgrid=True,\n", + " ticks=\"outside\")\n", + "\n", + "year_output_by_domain = go.Figure(fig)\n", + "\n", + "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_relative_growth\",x=group[0], color=group[-1], markers=True, labels={\n", + " record_col+\"_relative_growth\": 'Rel. growth
in co-publications (%)',\n", + " group[-1]: \"Domain\",\n", + " },\n", + " title=\"Relative growth in the output of co-publications\", template='plotly')\n", + "\n", + "fig.update_layout(hovermode='x unified',yaxis_tickformat='.0f%',font_family=\"Montserrat\")\n", + "fig.update_traces(hovertemplate='%{y:.0f}00%')\n", + "fig.add_shape(\n", + " # Rectangle with reference to the plot\n", + " type=\"rect\",\n", + " xref=\"paper\",\n", + " yref=\"paper\",\n", + " x0=0,\n", + " y0=0,\n", + " x1=1.0,\n", + " y1=1.0,\n", + " line=dict(\n", + " color=\"black\",\n", + " width=0.5,\n", + " )\n", + " )\n", + "fig.update_yaxes(\n", + " showgrid=True,\n", + " ticks=\"outside\")\n", + "fig.update_xaxes(\n", + " showgrid=True,\n", + " ticks=\"outside\")\n", + "\n", + "rel_output_by_domain = go.Figure(fig)\n", + "\n", + "\n", + "fig = px.area(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_cumsum\",x=group[0], color=group[-1],line_group=group[-1],\n", + " labels={\n", + " record_col+\"_cumsum\": 'Cumulative number of co-publications',\n", + " group[-1]: \"Domain\",\n", + " },\n", + " title=\"Cumulative number of co-publications\", template='plotly')\n", + "fig.update_traces(hovertemplate='%{y:d}')\n", + "fig.update_layout(hovermode='x unified')\n", + "fig.add_shape(\n", + " # Rectangle with reference to the plot\n", + " type=\"rect\",\n", + " xref=\"paper\",\n", + " yref=\"paper\",\n", + " x0=0,\n", + " y0=0,\n", + " x1=1.0,\n", + " y1=1.0,\n", + " line=dict(\n", + " color=\"black\",\n", + " width=0.5,\n", + " )\n", + " )\n", + "fig.update_yaxes(\n", + " showgrid=True,\n", + " ticks=\"outside\")\n", + "fig.update_xaxes(\n", + " showgrid=True,\n", + " ticks=\"outside\")\n", + "\n", + "cumsum_by_domain = go.Figure(fig)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 104, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "cliponaxis": 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"showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "tickformat": ".0f" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.0, + 0.45 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "range": [ + 2010.8, + 2022.2 + ] + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.0, + 0.375 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside" + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.55, + 1.0 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "range": [ + 2010.8, + 2022.2 + ] + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.0, + 0.375 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "zeroline": true, + "zerolinewidth": 0.5, + "zerolinecolor": "grey", + "tickformat": ".0%" + }, + "annotations": [ + { + "font": { + "size": 14 + }, + "showarrow": false, + "text": "Distribution of IPC classes", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 1.0, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 14 + }, + "showarrow": false, + "text": "Cumulative sum of related co-patents", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 1.0, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 14 + }, + "showarrow": false, + "text": "Related co-patents per year", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 0.375, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 14 + }, + "showarrow": false, + "text": "Relative growth of co-patents", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 0.375, + "yanchor": "bottom", + "yref": "paper" + } + ], + "barmode": "relative", + "font": { + 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from plotly.subplots import make_subplots\n", + "import plotly.graph_objects as go\n", + "\n", + "# dom_distr\n", + "# year_output_by_domain\n", + "# rel_output_by_domain\n", + "# cumsum_by_domain\n", + "\n", + "figsuper = make_subplots(rows=2, cols=2, subplot_titles=[\"Distribution of IPC classes\",\"Cumulative sum of related co-patents\",\n", + " \"Related co-patents per year\",\"Relative growth of co-patents\"])\n", + "\n", + "\n", + "for trace in list(dom_distr.select_traces()):\n", + " trace.showlegend=False\n", + " # trace.barmode\n", + " figsuper.add_trace(trace,\n", + " row=1, col=1\n", + " )\n", + "\n", + "for trace in list(cumsum_by_domain.select_traces()):\n", + " figsuper.add_trace(trace,\n", + " row=1, col=2\n", + " )\n", + "\n", + "for trace in list(year_output_by_domain.select_traces()):\n", + " trace.showlegend=False\n", + " figsuper.add_trace(trace,\n", + " row=2, col=1\n", + " )\n", + "\n", + "for trace in list(rel_output_by_domain.select_traces()):\n", + " trace.showlegend=False\n", + " figsuper.add_trace(trace,\n", + " row=2, col=2\n", + " )\n", + "\n", + "\n", + "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", + "figsuper.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "figsuper.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "\n", + "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", + "figsuper['layout']['xaxis1'].update(tickformat=\".0f\")\n", + "figsuper['layout']['yaxis2'].update(tickformat=\".0f\")\n", + "figsuper['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + "figsuper['layout'][f'xaxis'][\"range\"] = [0,210000]\n", + "for i in range(2,5):\n", + " figsuper['layout'][f'xaxis{i}'][\"range\"] = [2010.8,2022.2]\n", + "figsuper['layout'][\"font\"][\"size\"]=12\n", + "for a in figsuper['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 14\n", + "figsuper.show(config= dict(displayModeBar = False, responsive = True))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [ + { + "data": { + "text/plain": "1964" + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appln_ipc_f[\"filing_year\"].min()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [ + { + "data": { + "text/plain": "615464" + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appln_ipc_f[\"appln_id\"].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "data": { + "text/plain": "591442" + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appln_ipc_f.merge(cpc_def, on=\"cpc_id\")[\"appln_id\"].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 29, + "outputs": [ + { + "data": { + "text/plain": "array(['A', 'S', 'C'], dtype=object)" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appln_ipc_f[\"ipc_class_level\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 133, + "outputs": [], + "source": [ + "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n", + "# tls_801.head()\n", + "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n", + " (tls_801.ctry_code == 'CN')|\n", + " (tls_801.ctry_code == 'NO')|\n", + " (tls_801.ctry_code == 'CH')|\n", + " (tls_801.ctry_code == 'UK'))]\n", + "scope_countries = scope_df[\"ctry_code\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 134, + "outputs": [ + { + "data": { + "text/plain": "array(['AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI',\n 'FR', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'MT', 'NL',\n 'NO', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK'], dtype=object)" + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scope_countries" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [ + { + "data": { + "text/plain": " person_id appln_id applt_seq_nr invt_seq_nr\n0 1 413601768 1 0\n1 21 332015605 1 0\n2 21 333490084 1 0\n3 21 335903805 1 0\n4 76 352908776 1 0\n.. ... ... ... ...\n95 220 417147567 1 0\n96 220 417932952 1 0\n97 220 420712526 1 0\n98 220 420927947 1 0\n99 220 420947631 1 0\n\n[100 rows x 4 columns]", + "text/html": "
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" + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appln_pers.head(100)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'person_ctry_code'", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3649\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3648\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3649\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", + "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", + "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", + "\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[26], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m \u001B[43mappln_pers\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroupby\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfilter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mgr\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m|\u001B[39;49m\n\u001B[0;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_auth\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m 4\u001B[0m (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m 5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n", + "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\groupby\\generic.py:1741\u001B[0m, in \u001B[0;36mDataFrameGroupBy.filter\u001B[1;34m(self, func, dropna, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1738\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m name, group \u001B[38;5;129;01min\u001B[39;00m gen:\n\u001B[0;32m 1739\u001B[0m \u001B[38;5;28mobject\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__setattr__\u001B[39m(group, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mname\u001B[39m\u001B[38;5;124m\"\u001B[39m, name)\n\u001B[1;32m-> 1741\u001B[0m res \u001B[38;5;241m=\u001B[39m func(group, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1743\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 1744\u001B[0m res \u001B[38;5;241m=\u001B[39m res\u001B[38;5;241m.\u001B[39msqueeze()\n", + "Cell \u001B[1;32mIn[26], line 1\u001B[0m, in \u001B[0;36m\u001B[1;34m(gr)\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m appln_pers\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m 2\u001B[0m (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m 3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m 4\u001B[0m (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m 5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n", + "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\frame.py:3745\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3743\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m 3744\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3745\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3746\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m 3747\u001B[0m indexer \u001B[38;5;241m=\u001B[39m [indexer]\n", + "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3651\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3649\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m 3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3651\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 3652\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m 3653\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m 3654\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m 3655\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m 3656\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n", + "\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'" + ] + } + ], + "source": [ + "appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n", + " (gr[\"appln_auth\"]==\"CN\")).any())\n", + "appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(eu_countries)).any()|\n", + " (gr[\"appln_auth\"].isin(eu_countries))).any())\n", + "appln_scope[\"appln_id\"].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_scope" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_scope[appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_scope[~appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_cpc[\"cpc_id\"] = appln_cpc[\"cpc_class_symbol\"].str.replace(\" \",\"\")\n", + "\n", + "appln_cpc_merge = appln_cpc.merge(cpc_def,on='cpc_id')\n", + "print (f\"CPC Merge loss: {(1-len(appln_cpc_merge)/len(appln_cpc))*100}%\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "len(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")[\"appln_nr\"].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\")[\"appln_id\"].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "cpc_def[cpc_def[\"data_scope\"]==True]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln.sample(100)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_title.sample(100)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_data = appln.merge(appln_title, on=\"appln_id\")\n", + "appln_data.head()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_data[\"appln_auth\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_pers" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "pers_sector_primary = pers.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n", + " lambda x: pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})\n", + "persn = pers.merge(pers_sector_primary, on='han_id')\n", + "persn" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "pers_sector_primary" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "appln_merge = appln.merge(appln_title, on=\"appln_id\")#.merge(appln_pers,on=\"appln_id\")\n", + "appln_merge.to_excel(\"appln_data.xlsx\", index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "person_merge = appln_pers.merge(pers,on=\"person_id\")\n", + "person_merge.to_excel(\"person_data.xlsx\", index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], "source": [ "pers[\"person_ctry_code\"].unique()" ], diff --git a/PATSTAT/person_minipipe.ipynb b/PATSTAT/person_minipipe.ipynb new file mode 100644 index 0000000..a21687b --- /dev/null +++ b/PATSTAT/person_minipipe.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\PATSTAT\n", + "D:\\PATSTAT\n" + ] + } + ], + "source": [ + "\n", + "import dask\n", + "\n", + "dask.config.set(temporary_directory=r'D:\\PATSTAT\\dask_temp')\n", + "dask.config.set({'temporary_directory': r'D:\\PATSTAT\\dask_temp'})\n", + "dask.config.config\n", + "import dask.dataframe as dd\n", + "import os\n", + "\n", + "import os\n", + "print(os.getcwd()) # Prints the current working directory\n", + "\n", + "workdir_path=r\"D:\\PATSTAT\"\n", + "os.chdir(workdir_path)\n", + "print(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "tls_206 = dd.read_csv(\"table_tls206.csv\", low_memory=False)\n", + "tls_206.to_parquet(\"tls_206.parquet\")\n", + "# %%time\n", + "#Person data\n", + "tls_206_p = dd.read_parquet(\"tls_206.parquet\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "import pandas as pd\n", + "appln_pers_f = pd.read_csv(r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\PATSTAT\\first_round\\first-filings-with-persons-raw.csv\", header=None,\n", + " names=[\"appln_id\",\"appln_auth\",\"person_id\",\" invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "pers_id_scope =appln_pers_f[\"person_id\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "tls_206_scope = tls_206_p[tls_206_p['person_id'].isin(pers_id_scope)]\n", + "tls_206_scope.compute().to_csv(r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\PATSTAT\\first_round\\tls_206_scope.csv\",index=False)" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/WOS/wos_analysis/wos_analyses.ipynb b/WOS/wos_analysis/wos_analyses.ipynb index cd1c0cc..e056751 100644 --- a/WOS/wos_analysis/wos_analyses.ipynb +++ b/WOS/wos_analysis/wos_analyses.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "40038234", "metadata": {}, "outputs": [ @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "outputs": [], "source": [ "os.makedirs('plot_html',exist_ok=True)\n", @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "outputs": [ { "data": { @@ -238094,7 +238094,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -238129,7 +238129,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "outputs": [], "source": [ "aggr_dict = dict()\n", @@ -238152,14 +238152,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "outputs": [ { "data": { "text/plain": " Publication Years Record Count Scope\n12 2021 11677 CN+US\n13 2020 10774 CN+US\n14 2022 10551 CN+US\n15 2019 10098 CN+US\n16 2018 7907 CN+US\n17 2017 6064 CN+US\n18 2016 4827 CN+US\n19 2015 4051 CN+US\n20 2014 3321 CN+US\n21 2013 2541 CN+US\n22 2012 2002 CN+US\n23 2011 1629 CN+US\n36 2022 11079 EU+CN\n37 2021 10573 EU+CN\n38 2020 8492 EU+CN\n39 2019 7007 EU+CN\n40 2018 5243 EU+CN\n41 2017 3802 EU+CN\n42 2016 3046 EU+CN\n43 2015 2487 EU+CN\n44 2014 2123 EU+CN\n45 2013 1558 EU+CN\n46 2012 1226 EU+CN\n47 2011 1000 EU+CN\n48 2021 13659 EU+US\n49 2022 12705 EU+US\n50 2020 11524 EU+US\n51 2019 10610 EU+US\n52 2018 9044 EU+US\n53 2017 7587 EU+US\n54 2016 6714 EU+US\n55 2015 5911 EU+US\n56 2014 5177 EU+US\n57 2013 4525 EU+US\n58 2012 3982 EU+US\n59 2011 3663 EU+US", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication YearsRecord CountScope
12202111677CN+US
13202010774CN+US
14202210551CN+US
15201910098CN+US
1620187907CN+US
1720176064CN+US
1820164827CN+US
1920154051CN+US
2020143321CN+US
2120132541CN+US
2220122002CN+US
2320111629CN+US
36202211079EU+CN
37202110573EU+CN
3820208492EU+CN
3920197007EU+CN
4020185243EU+CN
4120173802EU+CN
4220163046EU+CN
4320152487EU+CN
4420142123EU+CN
4520131558EU+CN
4620121226EU+CN
4720111000EU+CN
48202113659EU+US
49202212705EU+US
50202011524EU+US
51201910610EU+US
5220189044EU+US
5320177587EU+US
5420166714EU+US
5520155911EU+US
5620145177EU+US
5720134525EU+US
5820123982EU+US
5920113663EU+US
\n
" }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -238171,6 +238171,48 @@ "collapsed": false } }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "Scope\nCN 735775\nCN+US 75442\nEU 792793\nEU+CN 57636\nEU+US 95101\nUS 592002\nglobal 2679651\nName: Record Count, dtype: int64" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aggr_analys.groupby(\"Scope\")[\"Record Count\"].sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " Publication Years Record Count Scope\n0 2022 137341 CN\n14 2022 10551 CN+US\n25 2022 101584 EU\n36 2022 11079 EU+CN\n49 2022 12705 EU+US\n60 2022 382731 global\n73 2022 72447 US", + "text/html": "
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Publication YearsRecord CountScope
02022137341CN
14202210551CN+US
252022101584EU
36202211079EU+CN
49202212705EU+US
602022382731global
73202272447US
\n
" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Filter aggr analys to 2022\n", + "aggr_analys[aggr_analys[\"Publication Years\"]==2022].copy()" + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "code", "execution_count": 8, @@ -239236,7 +239278,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -240285,7 +240327,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -242154,10 +242196,28 @@ "marker": { "symbol": "circle" }, - "mode": "lines+markers", + "mode": "lines+text+markers", "name": "", "orientation": "v", "showlegend": false, + "text": [ + 822.0, + 979.0, + 1263.0, + 1617.0, + 1889.0, + 2322.0, + 2975.0, + 4149.0, + 5593.0, + 6752.0, + 8116.0, + 9583.0 + ], + "textfont": { + "size": 12 + }, + "textposition": "top center", "x": [ 2011, 2012, @@ -242200,10 +242260,29 @@ "marker": { "symbol": "circle" }, - "mode": "lines+markers", + "mode": "lines+text+markers", "name": "", "orientation": "v", "showlegend": false, + "text": [ + 0.0, + 0.19099756690997566, + 0.5364963503649635, + 0.9671532846715328, + 1.2980535279805352, + 1.8248175182481752, + 2.619221411192214, + 4.047445255474453, + 5.804136253041363, + 7.214111922141119, + 8.873479318734793, + 10.658150851581508 + ], + "textfont": { + "size": 12 + }, + "textposition": "top center", + "texttemplate": "%{y:.0%}", "x": [ 2011, 2012, @@ -242979,365 +243058,1321 @@ ] ] }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0.55, + 1.0 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "range": [ + 2010.8, + 2022.5 + ] + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0.7777777777777778, + 1.0 + ], + "categoryorder": "total ascending", + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "range": [ + 0, + 11000 + ], + "tickformat": ".0f" + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.55, + 1.0 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "range": [ + 2010.8, + 2022.5 + ] + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0.3888888888888889, + 0.6111111111111112 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "zerolinecolor": "grey", + "tickformat": ".0%", + "range": [ + -1, + 13 + ] + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.55, + 1.0 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "range": [ + 2010.8, + 2022.5 + ] + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.0, + 0.22222222222222224 + ], + "showgrid": true, + "showline": true, + "linewidth": 1, + "linecolor": "black", + "mirror": true, + "ticks": "outside", + "tickformat": ".0f" + }, + "annotations": [ + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "Distribution of topics", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 1.0, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "Co-publications per year", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 1.0, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "Relative growth of co-publications", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 0.6111111111111112, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "Cumulative sum of co-publications", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 0.22222222222222224, + "yanchor": "bottom", + "yref": "paper" + } + ], + "barmode": "relative", + "font": { + "family": "Montserrat" + } + }, + "config": { + "displayModeBar": false, + "responsive": true, + "plotlyServerURL": "https://plotly.com" + } + }, + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# metrix_distr.show(config= dict(displayModeBar = False))\n", + "data = (wos.groupby(['Publication Year'])[record_col].nunique(dropna=False)\n", + " .reset_index()\n", + " .rename(columns={0:record_col}))\n", + "data[record_col+\"_relative_growth\"] = data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True)[record_col][0]\n", + "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", + "\n", + "data = data.sort_values(by =[\"Publication Year\"], ascending=[True])\n", + "data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n", + "\n", + "year_output = px.line(data,x=\"Publication Year\", y=record_col, markers=True, text=record_col)\n", + "year_output.update_traces(textposition='top center',textfont_size=12)\n", + "year_output.update_traces(hovertemplate='Year:%{x:d}
Number of co-publications:%{y:d}')\n", + "\n", + "year_rel_output = px.line(data,x=\"Publication Year\", y=record_col+\"_relative_growth\", markers=True, text=record_col+\"_relative_growth\")\n", + "year_rel_output.update_traces(textposition='top center',textfont_size=12, texttemplate='%{y:.0%}')\n", + "year_rel_output.update_traces(hovertemplate='Year:%{x:d}
Rel.growth in co-publications:%{y:.0%}')\n", + "\n", + "year_rel_cumsum = px.area(data,x=\"Publication Year\", y=record_col+\"_cumsum\")\n", + "year_rel_cumsum.update_traces(hovertemplate='Year:%{x:d}
Cumulative number co-publications:%{y:d}')\n", + "\n", + "\n", + "figsuper = make_subplots(rows=3, cols=2, subplot_titles=[\"Distribution of topics\",\n", + " \"Co-publications per year\",\"Relative growth of co-publications\",\n", + " \"Cumulative sum of co-publications\",],\n", + " specs=[\n", + " [{\"type\": \"domain\", \"rowspan\":3}, {\"type\": \"xy\"}],\n", + " [None,{\"type\": \"xy\"}],\n", + " [None, {\"type\": \"xy\"}]\n", + " ])\n", + "\n", + "\n", + "for trace in list(metrix_distr.select_traces()):\n", + " # trace.barmode\n", + " figsuper.add_trace(trace,\n", + " row=[1,2,3], col=1\n", + " )\n", + "\n", + "for trace in list(year_output.select_traces()):\n", + " figsuper.add_trace(trace,\n", + " row=1, col=2\n", + " )\n", + "\n", + "for trace in list(year_rel_output.select_traces()):\n", + " figsuper.add_trace(trace,\n", + " row=2, col=2\n", + " )\n", + "\n", + "for trace in list(year_rel_cumsum.select_traces()):\n", + " figsuper.add_trace(trace,\n", + " row=3, col=2\n", + " )\n", + "\n", + "# figsuper.update_layout(hovermode='x unified')\n", + "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", + "figsuper.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "figsuper.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", + "figsuper['layout']['yaxis2'].update(zerolinecolor='grey',tickformat=\".0%\")\n", + "for i in[\"xaxis\",\"xaxis2\",\"xaxis3\"]:\n", + " figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n", + "\n", + "figsuper['layout'][\"yaxis\"][\"range\"] = [0,11000]\n", + "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,13]\n", + "\n", + "figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n", + "figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n", + "\n", + "figsuper.show(config= dict(displayModeBar = False, responsive = True))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "hovertemplate": "Year:%{x:d}
Number of co-publications:%{y:d}", + "legendgroup": "", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+text+markers", + "name": "", + "orientation": "v", + "showlegend": false, + "text": [ + 822.0, + 979.0, + 1263.0, + 1617.0, + 1889.0, + 2322.0, + 2975.0, + 4149.0, + 5593.0, + 6752.0, + 8116.0, + 9583.0 + ], + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x", + "y": [ + 822, + 979, + 1263, + 1617, + 1889, + 2322, + 2975, + 4149, + 5593, + 6752, + 8116, + 9583 + ], + "yaxis": "y", + "type": "scatter", + "textfont": { + "size": 12 + }, + "textposition": "top center" + } + ], + "layout": { + "template": { + "data": { + "histogram2dcontour": [ + { + "type": "histogram2dcontour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + 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}, - "showarrow": false, - "text": "Relative growth of co-publications", - "x": 0.775, - "xanchor": "center", - "xref": "paper", - "y": 0.6111111111111112, - "yanchor": "bottom", - "yref": "paper" - }, - { - "font": { - "size": 16 - }, - "showarrow": false, - "text": "Cumulative sum of co-publications", - "x": 0.775, - "xanchor": "center", - "xref": "paper", - "y": 0.22222222222222224, - "yanchor": "bottom", - "yref": "paper" - } - ], - "barmode": "relative", - "font": { - "family": "Montserrat" + "margin": { + "t": 60 } }, "config": { - "displayModeBar": false, - "responsive": true, "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "# metrix_distr.show(config= dict(displayModeBar = False))\n", - "data = (wos.groupby(['Publication Year'])[record_col].nunique(dropna=False)\n", - " .reset_index()\n", - " .rename(columns={0:record_col}))\n", - "data[record_col+\"_relative_growth\"] = data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True)[record_col][0]\n", - "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", - "\n", - "data = data.sort_values(by =[\"Publication Year\"], ascending=[True])\n", - "data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n", - "\n", - "year_output = px.line(data,x=\"Publication Year\", y=record_col, markers=True)\n", - "year_output.update_traces(hovertemplate='Year:%{x:d}
Number of co-publications:%{y:d}')\n", - "\n", - "year_rel_output = px.line(data,x=\"Publication Year\", y=record_col+\"_relative_growth\", markers=True)\n", - "year_rel_output.update_traces(hovertemplate='Year:%{x:d}
Rel.growth in co-publications:%{y:.0%}')\n", - "\n", - "year_rel_cumsum = px.area(data,x=\"Publication Year\", y=record_col+\"_cumsum\")\n", - "year_rel_cumsum.update_traces(hovertemplate='Year:%{x:d}
Cumulative number co-publications:%{y:d}')\n", - "\n", - "\n", - "figsuper = make_subplots(rows=3, cols=2, subplot_titles=[\"Distribution of topics\",\n", - " \"Co-publications per year\",\"Relative growth of co-publications\",\n", - " \"Cumulative sum of co-publications\",],\n", - " specs=[\n", - " [{\"type\": \"domain\", \"rowspan\":3}, {\"type\": \"xy\"}],\n", - " [None,{\"type\": \"xy\"}],\n", - " [None, {\"type\": \"xy\"}]\n", - " ])\n", - "\n", - "\n", - "for trace in list(metrix_distr.select_traces()):\n", - " # trace.barmode\n", - " figsuper.add_trace(trace,\n", - " row=[1,2,3], col=1\n", - " )\n", - "\n", - "for trace in list(year_output.select_traces()):\n", - " figsuper.add_trace(trace,\n", - " row=1, col=2\n", - " )\n", - "\n", - "for trace in list(year_rel_output.select_traces()):\n", - " figsuper.add_trace(trace,\n", - " row=2, col=2\n", - " )\n", - "\n", - "for trace in list(year_rel_cumsum.select_traces()):\n", - " figsuper.add_trace(trace,\n", - " row=3, col=2\n", - " )\n", - "\n", - "# figsuper.update_layout(hovermode='x unified')\n", - "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", - "figsuper.update_yaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "figsuper.update_xaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", - "figsuper['layout']['yaxis2'].update(zerolinecolor='grey',tickformat=\".0%\")\n", - "for i in[\"xaxis\",\"xaxis2\",\"xaxis3\"]:\n", - " figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.2]\n", - "\n", - "figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n", - "figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n", - "\n", - "figsuper.show(config= dict(displayModeBar = False, responsive = True))" + "year_output" ], "metadata": { "collapsed": false @@ -243347,10 +244382,19 @@ "cell_type": "code", "execution_count": 20, "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [], "source": [ "# figsuper.write_html(f\"plot_html/Overall_distr&trends.html\",config= dict(displayModeBar = False, responsive = True))\n", "# figsuper.write_image(f\"plot_html/Overall_distr&trends.svg\",height=800,width=1600)\n", - "figsuper.write_image(f\"plot_html/Overall_distr&trends.png\",height=800,width=1600)" + "# figsuper.write_image(f\"plot_html/Overall_distr&trends.png\",height=800,width=1600)" ], "metadata": { "collapsed": false @@ -243359,6 +244403,15 @@ { "cell_type": "code", "execution_count": 21, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, "outputs": [ { "data": { @@ -245029,10 +246082,28 @@ "marker": { "symbol": "circle" }, - "mode": "lines+markers", + "mode": "lines+text+markers", "name": "", "orientation": "v", "showlegend": false, + "text": [ + 822.0, + 979.0, + 1263.0, + 1617.0, + 1889.0, + 2322.0, + 2975.0, + 4149.0, + 5593.0, + 6752.0, + 8116.0, + 9583.0 + ], + "textfont": { + "size": 12 + }, + "textposition": "top center", "x": [ 2011, 2012, @@ -245075,10 +246146,29 @@ "marker": { "symbol": "circle" }, - "mode": "lines+markers", + "mode": "lines+text+markers", "name": "", "orientation": "v", "showlegend": false, + "text": [ + 0.0, + 0.19099756690997566, + 0.5364963503649635, + 0.9671532846715328, + 1.2980535279805352, + 1.8248175182481752, + 2.619221411192214, + 4.047445255474453, + 5.804136253041363, + 7.214111922141119, + 8.873479318734793, + 10.658150851581508 + ], + "textfont": { + "size": 12 + }, + "textposition": "top center", + "texttemplate": "%{y:.0%}", "x": [ 2011, 2012, @@ -246048,7 +247138,7 @@ "mirror": true, "range": [ 2010.8, - 2022.2 + 2022.5 ], "showgrid": true, "showline": true, @@ -246064,6 +247154,10 @@ "linecolor": "black", "linewidth": 1, "mirror": true, + "range": [ + 0, + 11000 + ], "showgrid": true, "showline": true, "tickformat": ".0f", @@ -246080,7 +247174,7 @@ "mirror": true, "range": [ 2010.8, - 2022.2 + 2022.5 ], "showgrid": true, "showline": true, @@ -246095,6 +247189,10 @@ "linecolor": "black", "linewidth": 1, "mirror": true, + "range": [ + -1, + 13 + ], "showgrid": true, "showline": true, "tickformat": ".0%", @@ -246112,7 +247210,7 @@ "mirror": true, "range": [ 2010.8, - 2022.2 + 2022.5 ], "showgrid": true, "showline": true, @@ -246137,7 +247235,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -246166,7 +247264,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "14e82a73", "metadata": {}, "outputs": [], @@ -246210,7 +247308,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "id": "8cbe20ab", "metadata": {}, "outputs": [], @@ -246233,7 +247331,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "id": "05d0922a", "metadata": {}, "outputs": [], @@ -246338,7 +247436,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "id": "3a07c24d", "metadata": {}, "outputs": [ @@ -248389,7 +249487,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -248456,7 +249554,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "outputs": [], "source": [ "# figsuper.write_html(f\"plot_html/Domains_distr&trends.html\",config= dict(displayModeBar = False, responsive = True))" @@ -248467,7 +249565,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "outputs": [], "source": [ "# figsuper.write_image(f\"plot_html/Domains_distr&trends.svg\",height=800,width=1600)\n", @@ -248479,7 +249577,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "outputs": [ { "data": { @@ -250526,7 +251624,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -250548,7 +251646,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "id": "329b6889", "metadata": {}, "outputs": [ @@ -250557,7 +251655,7 @@ "text/plain": "Publication Year 2011 2012 2013 2014 2015 2016 2017 2018 \nDomain_English \nApplied Sciences 490 593 738 1031 1201 1535 1920 2808 \\\nArts & Humanities 0 0 0 4 1 3 7 4 \nEconomic & Social Sciences 20 22 29 28 34 40 84 105 \nHealth Sciences 116 120 155 184 216 243 321 403 \nMultidisciplinary 15 21 43 52 57 64 75 76 \nNatural Sciences 181 223 298 318 380 437 568 753 \n\nPublication Year 2019 2020 2021 2022 \nDomain_English \nApplied Sciences 3729 4446 5295 6199 \nArts & Humanities 11 11 16 13 \nEconomic & Social Sciences 160 211 252 375 \nHealth Sciences 611 755 1035 1182 \nMultidisciplinary 83 97 115 149 \nNatural Sciences 999 1232 1403 1665 ", "text/html": "
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Publication Year201120122013201420152016201720182019202020212022
Domain_English
Applied Sciences490593738103112011535192028083729444652956199
Arts & Humanities0004137411111613
Economic & Social Sciences20222928344084105160211252375
Health Sciences11612015518421624332140361175510351182
Multidisciplinary15214352576475768397115149
Natural Sciences181223298318380437568753999123214031665
\n
" }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -250571,7 +251669,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "id": "100f3002", "metadata": {}, "outputs": [], @@ -250583,7 +251681,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "id": "a8d24046", "metadata": {}, "outputs": [ @@ -250592,7 +251690,7 @@ "text/plain": "Publication Year 2011 2012 2013 2014 \nDomain_English \nApplied Sciences 59.610706 60.572012 58.432304 63.760049 \\\nArts & Humanities 0.000000 0.000000 0.000000 0.247372 \nEconomic & Social Sciences 2.433090 2.247191 2.296120 1.731602 \nHealth Sciences 14.111922 12.257406 12.272367 11.379097 \nMultidisciplinary 1.824818 2.145046 3.404592 3.215832 \nNatural Sciences 22.019465 22.778345 23.594616 19.666048 \n\nPublication Year 2015 2016 2017 2018 \nDomain_English \nApplied Sciences 63.578613 66.106804 64.537815 67.678959 \\\nArts & Humanities 0.052938 0.129199 0.235294 0.096409 \nEconomic & Social Sciences 1.799894 1.722653 2.823529 2.530730 \nHealth Sciences 11.434621 10.465116 10.789916 9.713184 \nMultidisciplinary 3.017470 2.756245 2.521008 1.831767 \nNatural Sciences 20.116464 18.819983 19.092437 18.148952 \n\nPublication Year 2019 2020 2021 2022 \nDomain_English \nApplied Sciences 66.672626 65.847156 65.241498 64.687467 \nArts & Humanities 0.196674 0.162915 0.197141 0.135657 \nEconomic & Social Sciences 2.860719 3.125000 3.104978 3.913180 \nHealth Sciences 10.924370 11.181872 12.752587 12.334342 \nMultidisciplinary 1.483998 1.436611 1.416954 1.554837 \nNatural Sciences 17.861613 18.246445 17.286841 17.374517 ", "text/html": "
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Publication Year201120122013201420152016201720182019202020212022
Domain_English
Applied Sciences59.61070660.57201258.43230463.76004963.57861366.10680464.53781567.67895966.67262665.84715665.24149864.687467
Arts & Humanities0.0000000.0000000.0000000.2473720.0529380.1291990.2352940.0964090.1966740.1629150.1971410.135657
Economic & Social Sciences2.4330902.2471912.2961201.7316021.7998941.7226532.8235292.5307302.8607193.1250003.1049783.913180
Health Sciences14.11192212.25740612.27236711.37909711.43462110.46511610.7899169.71318410.92437011.18187212.75258712.334342
Multidisciplinary1.8248182.1450463.4045923.2158323.0174702.7562452.5210081.8317671.4839981.4366111.4169541.554837
Natural Sciences22.01946522.77834523.59461619.66604820.11646418.81998319.09243718.14895217.86161318.24644517.28684117.374517
\n
" }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -250621,7 +251719,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "id": "0397eb85", "metadata": {}, "outputs": [], @@ -250633,7 +251731,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "id": "846596cf", "metadata": {}, "outputs": [], @@ -250654,15 +251752,96 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "id": "b3adb06a", "metadata": {}, + "outputs": [], + "source": [ + "wos_univ_locations = wos_univ.merge(wos_country_types, on=\"Country\")\n", + "wos_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\"]].drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "outputs": [ + { + "data": { + "text/plain": " Country_Type Country Institution_harm ISO3\n0 China China 7224 CHN\n1 EU Austria 213 AUT\n2 EU Belgium 261 BEL\n3 EU Bulgaria 36 BGR\n4 EU Croatia 54 HRV\n5 EU Cyprus 21 CYP\n6 EU Czech Republic 89 CZE\n7 EU Denmark 167 DNK\n8 EU Estonia 25 EST\n9 EU Finland 183 FIN\n10 EU France 1372 FRA\n11 EU Germany 1472 DEU\n12 EU Greece 202 GRC\n13 EU Hungary 99 HUN\n14 EU Ireland 121 IRL\n15 EU Italy 1057 ITA\n16 EU Latvia 14 LVA\n17 EU Lithuania 34 LTU\n18 EU Luxembourg 18 LUX\n19 EU Malta 5 MLT\n20 EU Netherlands 480 NLD\n21 EU Poland 228 POL\n22 EU Portugal 242 PRT\n23 EU Romania 166 ROU\n24 EU Slovakia 31 SVK\n25 EU Slovenia 39 SVN\n26 EU Spain 892 ESP\n27 EU Sweden 249 SWE\n28 Non-EU associate Norway 192 NOR\n29 Non-EU associate Switzerland 384 CHE\n30 Non-EU associate United Kingdom 1461 GBR", + "text/html": "
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Country_TypeCountryInstitution_harmISO3
0ChinaChina7224CHN
1EUAustria213AUT
2EUBelgium261BEL
3EUBulgaria36BGR
4EUCroatia54HRV
5EUCyprus21CYP
6EUCzech Republic89CZE
7EUDenmark167DNK
8EUEstonia25EST
9EUFinland183FIN
10EUFrance1372FRA
11EUGermany1472DEU
12EUGreece202GRC
13EUHungary99HUN
14EUIreland121IRL
15EUItaly1057ITA
16EULatvia14LVA
17EULithuania34LTU
18EULuxembourg18LUX
19EUMalta5MLT
20EUNetherlands480NLD
21EUPoland228POL
22EUPortugal242PRT
23EURomania166ROU
24EUSlovakia31SVK
25EUSlovenia39SVN
26EUSpain892ESP
27EUSweden249SWE
28Non-EU associateNorway192NOR
29Non-EU associateSwitzerland384CHE
30Non-EU associateUnited Kingdom1461GBR
\n
" + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = wos_univ_locations.groupby([\"Country_Type\",\"Country\"], as_index=False)[\"Institution_harm\"].nunique()\n", + "data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", + "data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 53, + "outputs": [], + "source": [ + "c_dict = {\"count\":\"Number of co-publications\",\n", + " \"percent_of_copubs\":\"Percent of co-publications\",\n", + " \"percent_contrib_in_copubs\":\"Contribution to co-publications\"}\n", + "\n", + "color_discrete_map= {'China': '#EF553B',\n", + " 'EU': '#636EFA',\n", + " 'Non-EU associate': '#00CC96'}\n", + "\n", + "fig = px.bar(\n", + " data[data[\"Country_Type\"]!=\"China\"],\n", + " x=\"Institution_harm\", y=\"ISO3\", color=\"Country_Type\", color_discrete_map=color_discrete_map,text_auto=True,\n", + " labels=dict({\n", + " record_col: 'Number of co-publications',\n", + " \"Institution_harm\": \"Number of unique participant institutes/organizations\",\n", + " \"Institution_harm_label\": \"Institution\",\n", + " \"Country_Type\":\"Country type\",\n", + " \"Eurovoc_Class\":\"Region\"\n", + " },**c_dict), template='plotly')\n", + "fig.update_layout(xaxis_tickformat='d',font_family=\"Montserrat\",\n", + " yaxis={'categoryorder':'total ascending'},\n", + " width=1000, height=1000,)\n", + "\n", + "\n", + "figsuper=fig\n", + "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", + "figsuper.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "figsuper.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", + "\n", + "for a in figsuper['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 14\n", + "figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n", + "\n", + "figsuper.update_layout(uniformtext_minsize=10)\n", + "figsuper.write_image(f\"plot_html/PPT_plots/europe_univ_unique.png\",height=800,width=800,scale = 4)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 54, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_5128\\1606125869.py:24: SettingWithCopyWarning:\n", + "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_19232\\1715665229.py:21: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", @@ -250670,7 +251849,7 @@ "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", - "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_5128\\1606125869.py:24: SettingWithCopyWarning:\n", + "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_19232\\1715665229.py:21: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", @@ -250678,7 +251857,7 @@ "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", - "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_5128\\1606125869.py:24: SettingWithCopyWarning:\n", + "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_19232\\1715665229.py:21: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", @@ -251983,15 +253162,13 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "wos_univ_locations = wos_univ.merge(wos_country_types, on=\"Country\")\n", - "wos_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\"]].drop_duplicates()\n", "\n", "collab_desc = wos_collabs[wos_collabs[\"Country\"]!=\"China\"][\"Country\"].value_counts().reset_index()\n", "collab_desc[\"percent_of_copubs\"] = collab_desc[\"count\"]/wos_collabs[record_col].nunique()#*100\n", @@ -252066,17 +253243,22 @@ "\n", "figsuper.update_layout(uniformtext_minsize=10)\n", "\n", - "figsuper.show(config= dict(displayModeBar = False, responsive = True))" - ] + "figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", + "\n", + "figsuper.write_image(f\"plot_html/europe_contribution_bar.png\",height=800,width=1600,scale = 4)" + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 214, "outputs": [], "source": [ "# figsuper.write_html(f\"plot_html/europe_contribution_bar.html\",config= dict(displayModeBar = False, responsive = True))\n", "# figsuper.write_image(f\"plot_html/europe_contribution_bar.svg\",height=800,width=1600)\n", - "figsuper.write_image(f\"plot_html/europe_contribution_bar.png\",height=800,width=1600)" + "figsuper.write_image(f\"plot_html/europe_contribution_bar.png\",height=800,width=1600,scale = 4)" ], "metadata": { "collapsed": false @@ -252084,9 +253266,19 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 55, "id": "c959287e", "metadata": {}, + "outputs": [], + "source": [ + "wos_collabs_EU = wos_univ_locations[~wos_univ_locations[\"Country_Type\"].isin([\"Other\",\"China\"])][[record_col,\"Country\"]].drop_duplicates()\n", + "wos_collabs_EU = wos_collabs_EU.merge(wos_collabs_EU, on=record_col)\n", + "EU_co_occur = pd.crosstab(wos_collabs_EU['Country_x'], wos_collabs_EU['Country_y'], values=wos_collabs_EU[record_col], aggfunc='nunique').fillna(0).astype(int)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 215, "outputs": [ { "data": { @@ -254032,17 +255224,13 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "wos_collabs_EU = wos_univ_locations[~wos_univ_locations[\"Country_Type\"].isin([\"Other\",\"China\"])][[record_col,\"Country\"]].drop_duplicates()\n", - "wos_collabs_EU = wos_collabs_EU.merge(wos_collabs_EU, on=record_col)\n", - "EU_co_occur = pd.crosstab(wos_collabs_EU['Country_x'], wos_collabs_EU['Country_y'], values=wos_collabs_EU[record_col], aggfunc='nunique').fillna(0).astype(int)\n", - "\n", "\n", "eu_list = wos_collabs_EU.groupby(['Country_x'])[record_col].count().sort_values(ascending=False).index\n", "# pre_fig = sns.clustermap(EU_co_occur)\n", @@ -254077,11 +255265,14 @@ "fig.update_xaxes(\n", " ticks=\"outside\")\n", "fig.show(config= dict(displayModeBar = False,responsive=True))" - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 216, "outputs": [], "source": [ "# fig.write_html(f\"plot_html/intraeurope_collabs.html\",config= dict(displayModeBar = False, responsive = True))\n", @@ -254094,7 +255285,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 217, "outputs": [ { "data": { @@ -256045,7 +257236,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -256076,7 +257267,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 218, "id": "f19501a9", "metadata": {}, "outputs": [ @@ -256085,7 +257276,7 @@ "text/plain": " Publication Year Country UT (Unique WOS ID) \n0 2011 Austria 22.0 \\\n1 2011 Belgium 34.0 \n2 2011 Bulgaria 4.0 \n3 2011 Croatia 1.0 \n4 2011 Cyprus 2.0 \n.. ... ... ... \n355 2022 Slovenia 40.0 \n356 2022 Spain 640.0 \n357 2022 Sweden 510.0 \n358 2022 Switzerland 447.0 \n359 2022 United Kingdom 4245.0 \n\n Publication Year_relative_growth UT (Unique WOS ID)_relative_growth \n0 2011 0.000000 \\\n1 2011 0.000000 \n2 2011 0.000000 \n3 2011 0.000000 \n4 2011 0.000000 \n.. ... ... \n355 2011 4.714286 \n356 2011 11.800000 \n357 2011 14.000000 \n358 2011 11.081081 \n359 2011 10.694215 \n\n UT (Unique WOS ID)_cumsum Country_Type Eurovoc_Class \n0 22.0 EU Western Europe \\\n1 34.0 EU Western Europe \n2 4.0 EU Eastern Europe \n3 1.0 EU Eastern Europe \n4 2.0 EU Southern Europe \n.. ... ... ... \n355 322.0 EU Eastern Europe \n356 2963.0 EU Southern Europe \n357 2656.0 EU Northern Europe \n358 2026.0 Non-EU associate Western Europe \n359 20419.0 Non-EU associate Western Europe \n\n year_unique pub_output_percent ISO3 \n0 822 0.026764 AUT \n1 822 0.041363 BEL \n2 822 0.004866 BGR \n3 822 0.001217 HRV \n4 822 0.002433 CYP \n.. ... ... ... \n355 9583 0.004174 SVN \n356 9583 0.066785 ESP \n357 9583 0.053219 SWE \n358 9583 0.046645 CHE \n359 9583 0.442972 GBR \n\n[360 rows x 11 columns]", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication YearCountryUT (Unique WOS ID)Publication Year_relative_growthUT (Unique WOS ID)_relative_growthUT (Unique WOS ID)_cumsumCountry_TypeEurovoc_Classyear_uniquepub_output_percentISO3
02011Austria22.020110.00000022.0EUWestern Europe8220.026764AUT
12011Belgium34.020110.00000034.0EUWestern Europe8220.041363BEL
22011Bulgaria4.020110.0000004.0EUEastern Europe8220.004866BGR
32011Croatia1.020110.0000001.0EUEastern Europe8220.001217HRV
42011Cyprus2.020110.0000002.0EUSouthern Europe8220.002433CYP
....................................
3552022Slovenia40.020114.714286322.0EUEastern Europe95830.004174SVN
3562022Spain640.0201111.8000002963.0EUSouthern Europe95830.066785ESP
3572022Sweden510.0201114.0000002656.0EUNorthern Europe95830.053219SWE
3582022Switzerland447.0201111.0810812026.0Non-EU associateWestern Europe95830.046645CHE
3592022United Kingdom4245.0201110.69421520419.0Non-EU associateWestern Europe95830.442972GBR
\n

360 rows × 11 columns

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" }, - "execution_count": 39, + "execution_count": 218, "metadata": {}, "output_type": "execute_result" } @@ -256118,7 +257309,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 219, "outputs": [ { "data": { @@ -262809,7 +264000,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -262931,7 +264122,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 220, "outputs": [ { "data": { @@ -269623,7 +270814,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -269645,7 +270836,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 221, "outputs": [], "source": [ "# figsuper.write_html(f\"plot_html/country_trends_overall.html\",config= dict(displayModeBar = False, responsive = True))\n", @@ -269658,7 +270849,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 222, "outputs": [ { "data": { @@ -273697,7 +274888,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -273744,7 +274935,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 223, "outputs": [], "source": [ "# fig.write_html(f\"plot_html/country_year_trends.html\",config= dict(displayModeBar = False, responsive = True))\n", @@ -273757,7 +274948,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 224, "outputs": [ { "data": { @@ -278067,7 +279258,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -278083,7 +279274,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 225, "outputs": [], "source": [ "fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", @@ -278128,7 +279319,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 226, "outputs": [ { "data": { @@ -282558,7 +283749,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -282584,7 +283775,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 227, "outputs": [], "source": [ "# fig.write_html(f\"plot_html/country_year_rel_trends.html\",config= dict(displayModeBar = False, responsive = True))\n", @@ -282597,7 +283788,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 228, "outputs": [], "source": [ "fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", @@ -282642,7 +283833,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 229, "outputs": [ { "data": { @@ -287072,7 +288263,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -287098,7 +288289,60 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 230, + "outputs": [ + { + "data": { + "text/plain": " Publication Year Country UT (Unique WOS ID) \n21 2011 Poland 17.0 \\\n51 2012 Poland 31.0 \n81 2013 Poland 37.0 \n111 2014 Poland 57.0 \n141 2015 Poland 73.0 \n171 2016 Poland 82.0 \n201 2017 Poland 98.0 \n231 2018 Poland 110.0 \n261 2019 Poland 138.0 \n291 2020 Poland 181.0 \n321 2021 Poland 276.0 \n351 2022 Poland 353.0 \n\n Publication Year_relative_growth UT (Unique WOS ID)_relative_growth \n21 2011 0.000000 \\\n51 2011 0.823529 \n81 2011 1.176471 \n111 2011 2.352941 \n141 2011 3.294118 \n171 2011 3.823529 \n201 2011 4.764706 \n231 2011 5.470588 \n261 2011 7.117647 \n291 2011 9.647059 \n321 2011 15.235294 \n351 2011 19.764706 \n\n UT (Unique WOS ID)_cumsum Country_Type Eurovoc_Class year_unique \n21 17.0 EU Eastern Europe 822 \\\n51 48.0 EU Eastern Europe 979 \n81 85.0 EU Eastern Europe 1263 \n111 142.0 EU Eastern Europe 1617 \n141 215.0 EU Eastern Europe 1889 \n171 297.0 EU Eastern Europe 2322 \n201 395.0 EU Eastern Europe 2975 \n231 505.0 EU Eastern Europe 4149 \n261 643.0 EU Eastern Europe 5593 \n291 824.0 EU Eastern Europe 6752 \n321 1100.0 EU Eastern Europe 8116 \n351 1453.0 EU Eastern Europe 9583 \n\n pub_output_percent ISO3 \n21 0.020681 POL \n51 0.031665 POL \n81 0.029295 POL \n111 0.035250 POL \n141 0.038645 POL \n171 0.035314 POL \n201 0.032941 POL \n231 0.026512 POL \n261 0.024674 POL \n291 0.026807 POL \n321 0.034007 POL \n351 0.036836 POL ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication YearCountryUT (Unique WOS ID)Publication Year_relative_growthUT (Unique WOS ID)_relative_growthUT (Unique WOS ID)_cumsumCountry_TypeEurovoc_Classyear_uniquepub_output_percentISO3
212011Poland17.020110.00000017.0EUEastern Europe8220.020681POL
512012Poland31.020110.82352948.0EUEastern Europe9790.031665POL
812013Poland37.020111.17647185.0EUEastern Europe12630.029295POL
1112014Poland57.020112.352941142.0EUEastern Europe16170.035250POL
1412015Poland73.020113.294118215.0EUEastern Europe18890.038645POL
1712016Poland82.020113.823529297.0EUEastern Europe23220.035314POL
2012017Poland98.020114.764706395.0EUEastern Europe29750.032941POL
2312018Poland110.020115.470588505.0EUEastern Europe41490.026512POL
2612019Poland138.020117.117647643.0EUEastern Europe55930.024674POL
2912020Poland181.020119.647059824.0EUEastern Europe67520.026807POL
3212021Poland276.0201115.2352941100.0EUEastern Europe81160.034007POL
3512022Poland353.0201119.7647061453.0EUEastern Europe95830.036836POL
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" + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data[\"Country\"]==\"Poland\"].sort_values(ascending=True, by='Publication Year')" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 231, + "outputs": [], + "source": [ + "px.bar(data[data[\"Publication Year\"]==2022].groupby(\"Eurovoc_Class\", as_index=False)[record_col+\"_relative_growth\"].median(), x=\"Eurovoc_Class\", y=record_col+\"_relative_growth\", color=\"Eurovoc_Class\").write_image(f\"plot_html/PPT_plots/country_relbar.png\",height=1600,width=1600,scale = 4)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 232, + "outputs": [ + { + "data": { + "text/plain": " Eurovoc_Class UT (Unique WOS ID)_relative_growth\n0 Eastern Europe 7.519481\n1 Northern Europe 12.000000\n2 Southern Europe 12.025000\n3 Western Europe 10.268293", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Eurovoc_ClassUT (Unique WOS ID)_relative_growth
0Eastern Europe7.519481
1Northern Europe12.000000
2Southern Europe12.025000
3Western Europe10.268293
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" + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data[\"Publication Year\"]==2022].groupby(\"Eurovoc_Class\", as_index=False)[record_col+\"_relative_growth\"].median()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 233, "id": "e4c50e14", "metadata": {}, "outputs": [ @@ -287107,7 +288351,7 @@ "text/plain": "Publication Year 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 \nCountry \nAustria 22 24 26 39 50 57 72 89 138 137 \\\nBelgium 34 38 40 65 71 81 90 133 179 213 \nBulgaria 4 5 8 9 7 19 21 18 10 25 \nCroatia 1 2 6 8 10 7 10 19 27 29 \nCyprus 2 1 5 5 5 5 8 7 15 28 \nCzech Republic 13 15 16 21 20 36 37 56 64 81 \nDenmark 35 33 40 59 68 74 101 195 234 245 \nEstonia 3 3 7 10 12 10 15 15 16 38 \nFinland 31 35 44 82 100 125 126 198 241 256 \nFrance 117 130 174 231 269 325 348 491 648 691 \nGermany 123 172 192 273 310 365 456 604 801 907 \nGreece 15 18 19 32 35 50 47 81 114 122 \nHungary 11 11 21 16 20 38 34 47 61 61 \nIreland 13 16 22 31 27 45 66 72 84 116 \nItaly 51 70 84 116 178 187 247 325 441 571 \nLatvia 0 0 1 0 1 8 10 15 10 9 \nLithuania 1 2 10 4 4 13 12 23 38 36 \nLuxembourg 2 3 3 1 8 9 13 15 18 22 \nMalta 1 0 0 0 1 1 0 0 6 2 \nNetherlands 72 64 77 103 139 166 220 297 408 470 \nNorway 30 42 60 76 67 88 104 134 222 253 \nPoland 17 31 37 57 73 82 98 110 138 181 \nPortugal 16 23 35 41 45 58 79 119 136 147 \nRomania 7 15 13 16 25 26 37 57 64 55 \nSlovakia 9 6 6 10 12 22 18 27 27 34 \nSlovenia 7 7 10 12 17 27 22 47 54 31 \nSpain 50 49 69 112 138 185 232 273 356 386 \nSweden 34 50 59 83 113 170 233 232 385 359 \nSwitzerland 37 50 54 74 74 95 155 195 233 263 \nUnited Kingdom 363 417 531 660 781 979 1350 1837 2430 3108 \n\nPublication Year 2021 2022 \nCountry \nAustria 185 205 \nBelgium 242 292 \nBulgaria 32 19 \nCroatia 33 35 \nCyprus 36 43 \nCzech Republic 93 123 \nDenmark 293 343 \nEstonia 45 39 \nFinland 289 380 \nFrance 807 858 \nGermany 1210 1386 \nGreece 139 181 \nHungary 83 90 \nIreland 167 187 \nItaly 641 811 \nLatvia 13 18 \nLithuania 38 38 \nLuxembourg 35 51 \nMalta 7 10 \nNetherlands 529 655 \nNorway 304 311 \nPoland 276 353 \nPortugal 204 212 \nRomania 48 62 \nSlovakia 36 45 \nSlovenia 48 40 \nSpain 473 640 \nSweden 428 510 \nSwitzerland 349 447 \nUnited Kingdom 3718 4245 ", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication Year201120122013201420152016201720182019202020212022
Country
Austria2224263950577289138137185205
Belgium34384065718190133179213242292
Bulgaria4589719211810253219
Croatia1268107101927293335
Cyprus2155558715283643
Czech Republic1315162120363756648193123
Denmark353340596874101195234245293343
Estonia337101210151516384539
Finland31354482100125126198241256289380
France117130174231269325348491648691807858
Germany12317219227331036545660480190712101386
Greece1518193235504781114122139181
Hungary111121162038344761618390
Ireland131622312745667284116167187
Italy517084116178187247325441571641811
Latvia00101810151091318
Lithuania12104413122338363838
Luxembourg233189131518223551
Malta1000110062710
Netherlands726477103139166220297408470529655
Norway304260766788104134222253304311
Poland17313757738298110138181276353
Portugal16233541455879119136147204212
Romania71513162526375764554862
Slovakia966101222182727343645
Slovenia7710121727224754314840
Spain504969112138185232273356386473640
Sweden34505983113170233232385359428510
Switzerland375054747495155195233263349447
United Kingdom363417531660781979135018372430310837184245
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" }, - "execution_count": 51, + "execution_count": 233, "metadata": {}, "output_type": "execute_result" } @@ -287119,7 +288363,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 234, "id": "e4e82db7", "metadata": {}, "outputs": [ @@ -287142,7 +288386,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 235, "id": "78bb0b4e", "metadata": {}, "outputs": [ @@ -287151,7 +288395,7 @@ "text/plain": "Publication Year 2011 2012 2013 2014 2015 \nCountry \nAustria 1.962533 1.801802 1.557819 1.736420 1.865672 \\\nBelgium 3.033006 2.852853 2.396645 2.894034 2.649254 \nBulgaria 0.356824 0.375375 0.479329 0.400712 0.261194 \nCroatia 0.089206 0.150150 0.359497 0.356189 0.373134 \nCyprus 0.178412 0.075075 0.299581 0.222618 0.186567 \nCzech Republic 1.159679 1.126126 0.958658 0.934996 0.746269 \nDenmark 3.122212 2.477477 2.396645 2.626892 2.537313 \nEstonia 0.267618 0.225225 0.419413 0.445236 0.447761 \nFinland 2.765388 2.627628 2.636309 3.650935 3.731343 \nFrance 10.437110 9.759760 10.425404 10.284951 10.037313 \nGermany 10.972346 12.912913 11.503895 12.154942 11.567164 \nGreece 1.338091 1.351351 1.138406 1.424755 1.305970 \nHungary 0.981267 0.825826 1.258238 0.712378 0.746269 \nIreland 1.159679 1.201201 1.318155 1.380232 1.007463 \nItaly 4.549509 5.255255 5.032954 5.164737 6.641791 \nLatvia 0.000000 0.000000 0.059916 0.000000 0.037313 \nLithuania 0.089206 0.150150 0.599161 0.178094 0.149254 \nLuxembourg 0.178412 0.225225 0.179748 0.044524 0.298507 \nMalta 0.089206 0.000000 0.000000 0.000000 0.037313 \nNetherlands 6.422837 4.804805 4.613541 4.585931 5.186567 \nNorway 2.676182 3.153153 3.594967 3.383793 2.500000 \nPoland 1.516503 2.327327 2.216896 2.537845 2.723881 \nPortugal 1.427297 1.726727 2.097064 1.825467 1.679104 \nRomania 0.624442 1.126126 0.778910 0.712378 0.932836 \nSlovakia 0.802855 0.450450 0.359497 0.445236 0.447761 \nSlovenia 0.624442 0.525526 0.599161 0.534283 0.634328 \nSpain 4.460303 3.678679 4.134212 4.986643 5.149254 \nSweden 3.033006 3.753754 3.535051 3.695459 4.216418 \nSwitzerland 3.300624 3.753754 3.235470 3.294746 2.761194 \nUnited Kingdom 32.381802 31.306306 31.815458 29.385574 29.141791 \n\nPublication Year 2016 2017 2018 2019 2020 \nCountry \nAustria 1.699970 1.689744 1.552958 1.816267 1.543488 \\\nBelgium 2.415747 2.112180 2.320712 2.355883 2.399730 \nBulgaria 0.566657 0.492842 0.314081 0.131614 0.281658 \nCroatia 0.208768 0.234687 0.331530 0.355357 0.326724 \nCyprus 0.149120 0.187749 0.122143 0.197420 0.315457 \nCzech Republic 1.073665 0.868341 0.977142 0.842327 0.912573 \nDenmark 2.206979 2.370336 3.402548 3.079758 2.760252 \nEstonia 0.298240 0.352030 0.261734 0.210582 0.428121 \nFinland 3.728005 2.957052 3.454894 3.171887 2.884182 \nFrance 9.692812 8.167097 8.567440 8.528560 7.785038 \nGermany 10.885774 10.701713 10.539173 10.542248 10.218567 \nGreece 1.491202 1.103027 1.413366 1.500395 1.374493 \nHungary 1.133313 0.797935 0.820101 0.802843 0.687247 \nIreland 1.342082 1.548932 1.256325 1.105554 1.306895 \nItaly 5.577095 5.796761 5.670913 5.804159 6.433078 \nLatvia 0.238592 0.234687 0.261734 0.131614 0.101397 \nLithuania 0.387712 0.281624 0.401326 0.500132 0.405588 \nLuxembourg 0.268416 0.305093 0.261734 0.236904 0.247859 \nMalta 0.029824 0.000000 0.000000 0.078968 0.022533 \nNetherlands 4.950790 5.163107 5.182342 5.369834 5.295178 \nNorway 2.624515 2.440742 2.338161 2.921822 2.850383 \nPoland 2.445571 2.299930 1.919386 1.816267 2.039207 \nPortugal 1.729794 1.854025 2.076426 1.789945 1.656151 \nRomania 0.775425 0.868341 0.994591 0.842327 0.619648 \nSlovakia 0.656129 0.422436 0.471122 0.355357 0.383055 \nSlovenia 0.805249 0.516311 0.820101 0.710713 0.349256 \nSpain 5.517447 5.444731 4.763567 4.685444 4.348806 \nSweden 5.070086 5.468200 4.048159 5.067123 4.044615 \nSwitzerland 2.833284 3.637644 3.402548 3.066596 2.963046 \nUnited Kingdom 29.197733 31.682704 32.053743 31.982101 35.015773 \n\nPublication Year 2021 2022 \nCountry \nAustria 1.712804 1.623248 \nBelgium 2.240533 2.312139 \nBulgaria 0.296269 0.150447 \nCroatia 0.305527 0.277140 \nCyprus 0.333302 0.340486 \nCzech Republic 0.861031 0.973949 \nDenmark 2.712712 2.715971 \nEstonia 0.416628 0.308813 \nFinland 2.675678 3.008948 \nFrance 7.471530 6.793887 \nGermany 11.202666 10.974741 \nGreece 1.286918 1.433209 \nHungary 0.768447 0.712645 \nIreland 1.546153 1.480719 \nItaly 5.934636 6.421728 \nLatvia 0.120359 0.142529 \nLithuania 0.351819 0.300895 \nLuxembourg 0.324044 0.403832 \nMalta 0.064809 0.079183 \nNetherlands 4.897695 5.186476 \nNorway 2.814554 2.462586 \nPoland 2.555319 2.795154 \nPortugal 1.888714 1.678676 \nRomania 0.444403 0.490934 \nSlovakia 0.333302 0.356323 \nSlovenia 0.444403 0.316731 \nSpain 4.379224 5.067701 \nSweden 3.962596 4.038324 \nSwitzerland 3.231182 3.539473 \nUnited Kingdom 34.422739 33.613113 ", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication Year201120122013201420152016201720182019202020212022
Country
Austria1.9625331.8018021.5578191.7364201.8656721.6999701.6897441.5529581.8162671.5434881.7128041.623248
Belgium3.0330062.8528532.3966452.8940342.6492542.4157472.1121802.3207122.3558832.3997302.2405332.312139
Bulgaria0.3568240.3753750.4793290.4007120.2611940.5666570.4928420.3140810.1316140.2816580.2962690.150447
Croatia0.0892060.1501500.3594970.3561890.3731340.2087680.2346870.3315300.3553570.3267240.3055270.277140
Cyprus0.1784120.0750750.2995810.2226180.1865670.1491200.1877490.1221430.1974200.3154570.3333020.340486
Czech Republic1.1596791.1261260.9586580.9349960.7462691.0736650.8683410.9771420.8423270.9125730.8610310.973949
Denmark3.1222122.4774772.3966452.6268922.5373132.2069792.3703363.4025483.0797582.7602522.7127122.715971
Estonia0.2676180.2252250.4194130.4452360.4477610.2982400.3520300.2617340.2105820.4281210.4166280.308813
Finland2.7653882.6276282.6363093.6509353.7313433.7280052.9570523.4548943.1718872.8841822.6756783.008948
France10.4371109.75976010.42540410.28495110.0373139.6928128.1670978.5674408.5285607.7850387.4715306.793887
Germany10.97234612.91291311.50389512.15494211.56716410.88577410.70171310.53917310.54224810.21856711.20266610.974741
Greece1.3380911.3513511.1384061.4247551.3059701.4912021.1030271.4133661.5003951.3744931.2869181.433209
Hungary0.9812670.8258261.2582380.7123780.7462691.1333130.7979350.8201010.8028430.6872470.7684470.712645
Ireland1.1596791.2012011.3181551.3802321.0074631.3420821.5489321.2563251.1055541.3068951.5461531.480719
Italy4.5495095.2552555.0329545.1647376.6417915.5770955.7967615.6709135.8041596.4330785.9346366.421728
Latvia0.0000000.0000000.0599160.0000000.0373130.2385920.2346870.2617340.1316140.1013970.1203590.142529
Lithuania0.0892060.1501500.5991610.1780940.1492540.3877120.2816240.4013260.5001320.4055880.3518190.300895
Luxembourg0.1784120.2252250.1797480.0445240.2985070.2684160.3050930.2617340.2369040.2478590.3240440.403832
Malta0.0892060.0000000.0000000.0000000.0373130.0298240.0000000.0000000.0789680.0225330.0648090.079183
Netherlands6.4228374.8048054.6135414.5859315.1865674.9507905.1631075.1823425.3698345.2951784.8976955.186476
Norway2.6761823.1531533.5949673.3837932.5000002.6245152.4407422.3381612.9218222.8503832.8145542.462586
Poland1.5165032.3273272.2168962.5378452.7238812.4455712.2999301.9193861.8162672.0392072.5553192.795154
Portugal1.4272971.7267272.0970641.8254671.6791041.7297941.8540252.0764261.7899451.6561511.8887141.678676
Romania0.6244421.1261260.7789100.7123780.9328360.7754250.8683410.9945910.8423270.6196480.4444030.490934
Slovakia0.8028550.4504500.3594970.4452360.4477610.6561290.4224360.4711220.3553570.3830550.3333020.356323
Slovenia0.6244420.5255260.5991610.5342830.6343280.8052490.5163110.8201010.7107130.3492560.4444030.316731
Spain4.4603033.6786794.1342124.9866435.1492545.5174475.4447314.7635674.6854444.3488064.3792245.067701
Sweden3.0330063.7537543.5350513.6954594.2164185.0700865.4682004.0481595.0671234.0446153.9625964.038324
Switzerland3.3006243.7537543.2354703.2947462.7611942.8332843.6376443.4025483.0665962.9630463.2311823.539473
United Kingdom32.38180231.30630631.81545829.38557429.14179129.19773331.68270432.05374331.98210135.01577334.42273933.613113
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" }, - "execution_count": 53, + "execution_count": 235, "metadata": {}, "output_type": "execute_result" } @@ -287163,7 +288407,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 236, "id": "42dc8be7", "metadata": {}, "outputs": [ @@ -287187,7 +288431,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 236, "id": "e7b754ea", "metadata": {}, "outputs": [], @@ -287195,7 +288439,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 237, "id": "48f2898f", "metadata": {}, "outputs": [], @@ -287205,7 +288449,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 237, "id": "3a9538e1", "metadata": {}, "outputs": [], @@ -287213,7 +288457,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 238, "id": "6bb0e68d", "metadata": {}, "outputs": [], @@ -287225,9 +288469,65 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 59, "id": "df8701eb", "metadata": {}, + "outputs": [], + "source": [ + "TOPN = 25\n", + "\n", + "\n", + "wos_univ_locations = wos_univ.merge(wos_country_types, on=\"Country\")\n", + "wos_univ_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\",\"Institution_harm\",\"Country_Type\",\"Eurovoc_Class\"]].drop_duplicates()\n", + "wos_univ_collabs[\"ISO3\"] = cc.pandas_convert(series=wos_univ_collabs[\"Country\"], to='ISO3')\n", + "wos_univ_collabs[\"Institution_harm_label\"] = wos_univ_collabs[\"Institution_harm\"] + \" (\"+wos_univ_collabs[\"ISO3\"]+ \")\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 60, + "outputs": [], + "source": [ + "\n", + "wos_univ_ch = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"China\"]\n", + "wos_univ_eu = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]!=\"China\"]\n", + "\n", + "wos_univ_eu_strict = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"EU\"]\n", + "\n", + "data_eu = (wos_univ_eu.groupby([\"Country\",\"Institution_harm_label\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", + " .sort_values(by=record_col,ascending=False).head(TOPN).copy()).sort_values(by=\"Country_Type\")\n", + "\n", + "data_eu_strict = (wos_univ_eu_strict.groupby([\"Country\",\"Institution_harm_label\",\"Eurovoc_Class\"], as_index=False)[record_col].nunique()\n", + " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", + "\n", + "data_ch = (wos_univ_ch.groupby([\"Country\",\"Institution_harm\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", + " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 239, "outputs": [ { "data": { @@ -288332,7 +289632,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -289368,7 +290668,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -290332,36 +291632,13 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "TOPN = 25\n", - "\n", - "\n", - "wos_univ_locations = wos_univ.merge(wos_country_types, on=\"Country\")\n", - "wos_univ_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\",\"Institution_harm\",\"Country_Type\",\"Eurovoc_Class\"]].drop_duplicates()\n", - "wos_univ_collabs[\"ISO3\"] = cc.pandas_convert(series=wos_univ_collabs[\"Country\"], to='ISO3')\n", - "wos_univ_collabs[\"Institution_harm_label\"] = wos_univ_collabs[\"Institution_harm\"] + \" (\"+wos_univ_collabs[\"ISO3\"]+ \")\"\n", - "\n", - "\n", - "wos_univ_ch = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"China\"]\n", - "wos_univ_eu = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]!=\"China\"]\n", - "\n", - "wos_univ_eu_strict = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"EU\"]\n", - "\n", - "data_eu = (wos_univ_eu.groupby([\"Country\",\"Institution_harm_label\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", - " .sort_values(by=record_col,ascending=False).head(TOPN).copy()).sort_values(by=\"Country_Type\")\n", - "\n", - "data_eu_strict = (wos_univ_eu_strict.groupby([\"Country\",\"Institution_harm_label\",\"Eurovoc_Class\"], as_index=False)[record_col].nunique()\n", - " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", - "\n", - "data_ch = (wos_univ_ch.groupby([\"Country\",\"Institution_harm\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", - " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", - "\n", "\n", "for data,c_scope, y_lab, col_by, pat in zip([data_eu,data_eu_strict,data_ch],\n", " [\"European countries in scope\",\"EU-28 only\",\"China\"],\n", @@ -290414,17 +291691,20 @@ " figsuper_ppt.update_xaxes(tickfont=dict(size=s))\n", " figsuper_ppt.write_image(f\"plot_html/PPT_plots/overall_inst_collab_bar_{c_scope}.png\",height=900,width=1000,scale = 4)\n", " # figsuper_ppt.show()" - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 240, "outputs": [ { "data": { "text/plain": "Layout({\n 'barmode': 'relative',\n 'font': {'family': 'Montserrat', 'size': 22},\n 'height': 1000,\n 'legend': {'title': {'text': 'Country type'}, 'tracegroupgap': 0},\n 'shapes': [{'line': {'color': 'black', 'width': 0.5},\n 'type': 'rect',\n 'x0': 0,\n 'x1': 1.0,\n 'xref': 'paper',\n 'y0': 0,\n 'y1': 1.0,\n 'yref': 'paper'}],\n 'template': '...',\n 'title': {'text': 'Most visible institutions (top 25 within China)'},\n 'width': 1000,\n 'xaxis': {'anchor': 'y',\n 'domain': [0.0, 1.0],\n 'showgrid': True,\n 'tickfont': {'size': 16},\n 'tickformat': 'd',\n 'ticks': 'outside',\n 'title': {'text': 'Number of co-publications'}},\n 'yaxis': {'anchor': 'x',\n 'categoryorder': 'total ascending',\n 'domain': [0.0, 1.0],\n 'showgrid': True,\n 'tickfont': {'size': 16},\n 'ticks': 'outside',\n 'title': {'text': 'Institution'}}\n})" }, - "execution_count": 58, + "execution_count": 240, "metadata": {}, "output_type": "execute_result" } @@ -290438,7 +291718,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 61, "id": "31a0769d", "metadata": {}, "outputs": [], @@ -290451,9 +291731,64 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 66, + "outputs": [ + { + "data": { + "text/plain": "9566" + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 63, "id": "2c5d1d94", "metadata": {}, + "outputs": [], + "source": [ + "subfilter = ((wos_univ_dipol[\"Institution_harm_label_eu\"].isin(data_eu[\"Institution_harm_label\"]))&\n", + " (wos_univ_dipol[\"Institution_harm_ch\"].isin(data_ch[\"Institution_harm\"])))\n", + "\n", + "fig = px.parallel_categories(wos_univ_dipol[subfilter][[\"Country_eu\",\"Institution_harm_eu\",\"Domain_English\",\"Institution_harm_ch\"]])\n", + "# fig.show()\n", + "sub_df =wos_univ_dipol[subfilter]\n", + "\n", + "inst_co_occur = pd.crosstab(sub_df['Institution_harm_label_eu'], sub_df['Institution_harm_ch'],\n", + " values=sub_df[record_col], aggfunc='nunique').fillna(0).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "outputs": [ + { + "data": { + "text/plain": "Institution_harm_ch Beihang Univ Beijing Inst Technol \nInstitution_harm_label_eu \nAalborg Univ (DNK) 12 6 \\\nBrunel Univ London (GBR) 45 10 \nCardiff Univ (GBR) 10 8 \nDelft Univ Technol (NLD) 9 36 \nImperial Coll London (GBR) 60 25 \nKings Coll London (GBR) 10 6 \nQueen Mary Univ London (GBR) 22 7 \nRoyal Inst Technol (SWE) 17 27 \nSwiss Fed Inst Technol (CHE) 41 17 \nTech Univ Munich (DEU) 11 20 \nUCL (GBR) 11 12 \nUniv Birmingham (GBR) 13 6 \nUniv Cambridge (GBR) 22 19 \nUniv Edinburgh (GBR) 24 3 \nUniv Lancaster (GBR) 27 4 \nUniv Leicester (GBR) 22 3 \nUniv Liverpool (GBR) 9 13 \nUniv Manchester (GBR) 38 10 \nUniv Nottingham (GBR) 6 6 \nUniv Oslo (NOR) 9 7 \nUniv Oxford (GBR) 20 11 \nUniv Sheffield (GBR) 23 15 \nUniv Southampton (GBR) 51 22 \nUniv Surrey (GBR) 11 5 \nUniv Warwick (GBR) 9 9 \n\nInstitution_harm_ch Beijing Univ Posts & Telecommun \nInstitution_harm_label_eu \nAalborg Univ (DNK) 21 \\\nBrunel Univ London (GBR) 18 \nCardiff Univ (GBR) 2 \nDelft Univ Technol (NLD) 8 \nImperial Coll London (GBR) 18 \nKings Coll London (GBR) 26 \nQueen Mary Univ London (GBR) 102 \nRoyal Inst Technol (SWE) 31 \nSwiss Fed Inst Technol (CHE) 14 \nTech Univ Munich (DEU) 6 \nUCL (GBR) 41 \nUniv Birmingham (GBR) 3 \nUniv Cambridge (GBR) 10 \nUniv Edinburgh (GBR) 9 \nUniv Lancaster (GBR) 9 \nUniv Leicester (GBR) 10 \nUniv Liverpool (GBR) 1 \nUniv Manchester (GBR) 16 \nUniv Nottingham (GBR) 3 \nUniv Oslo (NOR) 40 \nUniv Oxford (GBR) 13 \nUniv Sheffield (GBR) 12 \nUniv Southampton (GBR) 20 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Manchester (GBR) 13 \nUniv Nottingham (GBR) 12 \nUniv Oslo (NOR) 10 \nUniv Oxford (GBR) 24 \nUniv Sheffield (GBR) 12 \nUniv Southampton (GBR) 17 \nUniv Surrey (GBR) 9 \nUniv Warwick (GBR) 13 \n\nInstitution_harm_ch Northwestern Polytech Univ ... Tongji Univ \nInstitution_harm_label_eu ... \nAalborg Univ (DNK) 27 ... 7 \\\nBrunel Univ London (GBR) 18 ... 60 \nCardiff Univ (GBR) 6 ... 10 \nDelft Univ Technol (NLD) 12 ... 19 \nImperial Coll London (GBR) 7 ... 16 \nKings Coll London (GBR) 1 ... 9 \nQueen Mary Univ London (GBR) 6 ... 7 \nRoyal Inst Technol (SWE) 5 ... 20 \nSwiss Fed Inst Technol (CHE) 4 ... 9 \nTech Univ Munich (DEU) 17 ... 61 \nUCL (GBR) 9 ... 19 \nUniv Birmingham (GBR) 7 ... 3 \nUniv Cambridge (GBR) 10 ... 14 \nUniv Edinburgh (GBR) 10 ... 10 \nUniv Lancaster (GBR) 8 ... 12 \nUniv Leicester (GBR) 3 ... 5 \nUniv Liverpool (GBR) 8 ... 39 \nUniv Manchester (GBR) 15 ... 3 \nUniv Nottingham (GBR) 2 ... 7 \nUniv Oslo (NOR) 3 ... 1 \nUniv Oxford (GBR) 3 ... 12 \nUniv Sheffield (GBR) 4 ... 4 \nUniv Southampton (GBR) 13 ... 17 \nUniv Surrey (GBR) 10 ... 1 \nUniv Warwick (GBR) 3 ... 14 \n\nInstitution_harm_ch Tsinghua Univ Univ Chinese Acad Sci \nInstitution_harm_label_eu \nAalborg Univ (DNK) 12 13 \\\nBrunel Univ London (GBR) 49 34 \nCardiff Univ (GBR) 69 21 \nDelft Univ Technol (NLD) 27 10 \nImperial Coll London (GBR) 111 47 \nKings Coll London (GBR) 77 26 \nQueen Mary Univ London (GBR) 67 30 \nRoyal Inst Technol (SWE) 71 32 \nSwiss Fed Inst Technol (CHE) 76 39 \nTech Univ Munich (DEU) 27 11 \nUCL (GBR) 151 53 \nUniv Birmingham (GBR) 96 33 \nUniv Cambridge (GBR) 131 41 \nUniv Edinburgh (GBR) 91 58 \nUniv Lancaster (GBR) 83 42 \nUniv Leicester (GBR) 15 6 \nUniv Liverpool (GBR) 82 34 \nUniv Manchester (GBR) 99 57 \nUniv Nottingham (GBR) 10 13 \nUniv Oslo (NOR) 65 29 \nUniv Oxford (GBR) 125 58 \nUniv Sheffield (GBR) 78 32 \nUniv Southampton (GBR) 81 31 \nUniv Surrey (GBR) 18 17 \nUniv Warwick (GBR) 103 27 \n\nInstitution_harm_ch Univ Elect Sci & Technol China Univ Hong Kong \nInstitution_harm_label_eu \nAalborg Univ (DNK) 78 14 \\\nBrunel Univ London (GBR) 14 3 \nCardiff Univ (GBR) 1 14 \nDelft Univ Technol (NLD) 16 7 \nImperial Coll London (GBR) 24 53 \nKings Coll London (GBR) 30 92 \nQueen Mary Univ London (GBR) 27 68 \nRoyal Inst Technol (SWE) 23 57 \nSwiss Fed Inst Technol (CHE) 17 13 \nTech Univ Munich (DEU) 10 15 \nUCL (GBR) 25 115 \nUniv Birmingham (GBR) 9 97 \nUniv Cambridge (GBR) 5 110 \nUniv Edinburgh (GBR) 5 91 \nUniv Lancaster (GBR) 6 59 \nUniv Leicester (GBR) 10 19 \nUniv Liverpool (GBR) 8 81 \nUniv Manchester (GBR) 2 74 \nUniv Nottingham (GBR) 2 11 \nUniv Oslo (NOR) 45 78 \nUniv Oxford (GBR) 31 115 \nUniv Sheffield (GBR) 6 63 \nUniv Southampton (GBR) 17 25 \nUniv Surrey (GBR) 20 7 \nUniv Warwick (GBR) 32 71 \n\nInstitution_harm_ch Univ Sci & Technol China Wuhan Univ \nInstitution_harm_label_eu \nAalborg Univ (DNK) 6 9 \\\nBrunel Univ London (GBR) 0 6 \nCardiff Univ (GBR) 17 10 \nDelft Univ Technol (NLD) 5 22 \nImperial Coll London (GBR) 15 39 \nKings Coll London (GBR) 63 33 \nQueen Mary Univ London (GBR) 71 12 \nRoyal Inst Technol (SWE) 76 11 \nSwiss Fed Inst Technol (CHE) 13 13 \nTech Univ Munich (DEU) 25 40 \nUCL (GBR) 82 53 \nUniv Birmingham (GBR) 100 18 \nUniv Cambridge (GBR) 76 31 \nUniv Edinburgh (GBR) 82 34 \nUniv Lancaster (GBR) 74 32 \nUniv Leicester (GBR) 6 11 \nUniv Liverpool (GBR) 69 29 \nUniv Manchester (GBR) 85 24 \nUniv Nottingham (GBR) 9 16 \nUniv Oslo (NOR) 70 44 \nUniv Oxford (GBR) 89 49 \nUniv Sheffield (GBR) 92 8 \nUniv Southampton (GBR) 6 26 \nUniv Surrey (GBR) 11 6 \nUniv Warwick (GBR) 80 22 \n\nInstitution_harm_ch Xi An Jiao Tong Univ Xidian Univ Zhejiang Univ \nInstitution_harm_label_eu \nAalborg Univ (DNK) 14 5 35 \nBrunel Univ London (GBR) 20 2 31 \nCardiff Univ (GBR) 12 6 21 \nDelft Univ Technol (NLD) 8 0 37 \nImperial Coll London (GBR) 30 7 106 \nKings Coll London (GBR) 16 3 28 \nQueen Mary Univ London (GBR) 8 17 19 \nRoyal Inst Technol (SWE) 9 19 37 \nSwiss Fed Inst Technol (CHE) 9 8 48 \nTech Univ Munich (DEU) 12 7 27 \nUCL (GBR) 22 12 35 \nUniv Birmingham (GBR) 10 58 21 \nUniv Cambridge (GBR) 10 2 53 \nUniv Edinburgh (GBR) 21 0 21 \nUniv Lancaster (GBR) 6 16 18 \nUniv Leicester (GBR) 11 12 14 \nUniv Liverpool (GBR) 22 0 24 \nUniv Manchester (GBR) 14 12 18 \nUniv Nottingham (GBR) 7 8 22 \nUniv Oslo (NOR) 6 15 14 \nUniv Oxford (GBR) 21 4 48 \nUniv Sheffield (GBR) 10 25 15 \nUniv Southampton (GBR) 5 9 49 \nUniv Surrey (GBR) 5 28 11 \nUniv Warwick (GBR) 9 24 8 \n\n[25 rows x 25 columns]", + "text/html": "
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Institution_harm_chBeihang UnivBeijing Inst TechnolBeijing Univ Posts & TelecommunChinese Acad SciChinese Univ Hong KongFudan UnivHarbin Inst TechnolHong Kong Polytech UnivHuazhong Univ Sci & TechnolNorthwestern Polytech Univ...Tongji UnivTsinghua UnivUniv Chinese Acad SciUniv Elect Sci & Technol ChinaUniv Hong KongUniv Sci & Technol ChinaWuhan UnivXi An Jiao Tong UnivXidian UnivZhejiang Univ
Institution_harm_label_eu
Aalborg Univ (DNK)126213058291927...7121378146914535
Brunel Univ London (GBR)45101826121186918...6049341430620231
Cardiff Univ (GBR)108258134215106...106921114171012621
Delft Univ Technol (NLD)9368647232226712...1927101675228037
Imperial Coll London (GBR)6025188967801742187...161114724531539307106
Kings Coll London (GBR)10626939039302151...977263092633316328
Queen Mary Univ London (GBR)22710210472174696...767302768711281719
Royal Inst Technol (SWE)17273111961161213165...2071322357761191937
Swiss Fed Inst Technol (CHE)411714641726121584...97639171313139848
Tech Univ Munich (DEU)1120679153825161217...6127111015254012727
UCL (GBR)111241164122421925169...1915153251158253221235
Univ Birmingham (GBR)136310167119997...3963399710018105821
Univ Cambridge (GBR)221910147115474202510...14131415110763110253
Univ Edinburgh (GBR)24391458830917810...109158591823421021
Univ Lancaster (GBR)274915460742018...128342659743261618
Univ Leicester (GBR)2231033131419953...51561019611111214
Univ Liverpool (GBR)913111875141920148...398234881692922024
Univ Manchester (GBR)38101615963819201315...399572748524141218
Univ Nottingham (GBR)663461127721122...710132119167822
Univ Oslo (NOR)97401026618217103...165294578704461514
Univ Oxford (GBR)20111318910055740243...121255831115894921448
Univ Sheffield (GBR)23151212868151021124...47832663928102515
Univ Southampton (GBR)5122206618325161713...17813117256265949
Univ Surrey (GBR)11538395101416910...1181720711652811
Univ Warwick (GBR)992210468381118133...1410327327180229248
\n

25 rows × 25 columns

\n
" + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inst_co_occur" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 242, "outputs": [ { "data": { @@ -291197,7 +292532,7 @@ "type": "heatmap", "xaxis": "x", "yaxis": "y", - "hovertemplate": "Institute (CH): %{x}
Institute (EU): %{y}
Co-publication: %{z}" + "hovertemplate": "Institute (CN): %{x}
Institute (EU): %{y}
Co-publication: %{z}" } ], "layout": { @@ -292026,7 +293361,7 @@ "scaleanchor": "y", "constrain": "domain", "title": { - "text": "Institute (CH)" + "text": "Institute (CN)" }, "showgrid": false, "tickangle": -45, @@ -292114,22 +293449,13 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "subfilter = ((wos_univ_dipol[\"Institution_harm_label_eu\"].isin(data_eu[\"Institution_harm_label\"]))&\n", - " (wos_univ_dipol[\"Institution_harm_ch\"].isin(data_ch[\"Institution_harm\"])))\n", - "\n", - "fig = px.parallel_categories(wos_univ_dipol[subfilter][[\"Country_eu\",\"Institution_harm_eu\",\"Domain_English\",\"Institution_harm_ch\"]])\n", - "# fig.show()\n", - "sub_df =wos_univ_dipol[subfilter]\n", - "\n", - "inst_co_occur = pd.crosstab(sub_df['Institution_harm_label_eu'], sub_df['Institution_harm_ch'],\n", - " values=sub_df[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", "eu_list = sub_df.groupby(['Institution_harm_label_eu'])[record_col].count().sort_values(ascending=False).index\n", "ch_list = sub_df.groupby(['Institution_harm_ch'])[record_col].count().sort_values(ascending=False).index\n", @@ -292140,7 +293466,7 @@ "data = np.where(mask,inst_co_occur,inst_co_occur)\n", "\n", "fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),text_auto=True,\n", " y=list(inst_co_occur.index), title=f\"Most visible institutions (top {TOPN} within Europe)\"\n", " )\n", @@ -292162,11 +293488,14 @@ " ticks=\"outside\")\n", "\n", "fig.show(config= dict(displayModeBar = False, responsive = True))" - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 243, "outputs": [], "source": [ "# fig.write_html(f\"plot_html/overall_inst_collab_europe.html\",config= dict(displayModeBar = False, responsive = True))\n", @@ -292179,7 +293508,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 244, "outputs": [ { "data": { @@ -292187,7 +293516,7 @@ "data": [ { "coloraxis": "coloraxis", - "hovertemplate": "Institute (CH): %{x}
Institute (EU): %{y}
Co-publication: %{z}", + "hovertemplate": "Institute (CN): %{x}
Institute (EU): %{y}
Co-publication: %{z}", "name": "0", "texttemplate": "%{z}", "x": [ @@ -293816,7 +295145,7 @@ "tickangle": 45, "ticks": "outside", "title": { - "text": "Institute (CH)" + "text": "Institute (CN)" }, "tickfont": { "size": 16 @@ -293847,7 +295176,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -293872,13 +295201,13 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 245, "outputs": [ { "data": { "text/plain": "Institution_harm_label_eu\nAalborg Univ (DNK) 387\nBrunel Univ London (GBR) 489\nCardiff Univ (GBR) 382\nDelft Univ Technol (NLD) 435\nImperial Coll London (GBR) 1224\nKings Coll London (GBR) 932\nQueen Mary Univ London (GBR) 911\nRoyal Inst Technol (SWE) 878\nSwiss Fed Inst Technol (CHE) 621\nTech Univ Munich (DEU) 610\nUCL (GBR) 1382\nUniv Birmingham (GBR) 888\nUniv Cambridge (GBR) 1187\nUniv Edinburgh (GBR) 1060\nUniv Lancaster (GBR) 819\nUniv Leicester (GBR) 340\nUniv Liverpool (GBR) 901\nUniv Manchester (GBR) 986\nUniv Nottingham (GBR) 320\nUniv Oslo (NOR) 857\nUniv Oxford (GBR) 1377\nUniv Sheffield (GBR) 853\nUniv Southampton (GBR) 671\nUniv Surrey (GBR) 352\nUniv Warwick (GBR) 914\nName: UT (Unique WOS ID), dtype: int64" }, - "execution_count": 63, + "execution_count": 245, "metadata": {}, "output_type": "execute_result" } @@ -293892,14 +295221,14 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 246, "outputs": [ { "data": { "text/plain": " UT (Unique WOS ID) Country_eu Institution_harm_eu \n102 WOS:000296783400024 United Kingdom Univ Nottingham \\\n103 WOS:000296783400024 United Kingdom Cardiff Univ \n105 WOS:000296783400024 United Kingdom Univ Edinburgh \n146 WOS:000298320400037 United Kingdom Imperial Coll London \n148 WOS:000298320400037 United Kingdom Brunel Univ London \n... ... ... ... \n332571 WOS:000897707600137 Switzerland Swiss Fed Inst Technol \n332586 WOS:000902112600001 Switzerland Swiss Fed Inst Technol \n332594 WOS:000906995300001 Switzerland Swiss Fed Inst Technol \n332613 WOS:000922928206021 Switzerland Swiss Fed Inst Technol \n332615 WOS:000923379400006 Switzerland Swiss Fed Inst Technol \n\n Country_Type_eu Eurovoc_Class_eu ISO3_eu \n102 Non-EU associate Western Europe GBR \\\n103 Non-EU associate Western Europe GBR \n105 Non-EU associate Western Europe GBR \n146 Non-EU associate Western Europe GBR \n148 Non-EU associate Western Europe GBR \n... ... ... ... \n332571 Non-EU associate Western Europe CHE \n332586 Non-EU associate Western Europe CHE \n332594 Non-EU associate Western Europe CHE \n332613 Non-EU associate Western Europe CHE \n332615 Non-EU associate Western Europe CHE \n\n Institution_harm_label_eu Country_ch Institution_harm_ch \n102 Univ Nottingham (GBR) China Univ Sci & Technol China \\\n103 Cardiff Univ (GBR) China Univ Sci & Technol China \n105 Univ Edinburgh (GBR) China Univ Sci & Technol China \n146 Imperial Coll London (GBR) China Peking Univ \n148 Brunel Univ London (GBR) China Peking Univ \n... ... ... ... \n332571 Swiss Fed Inst Technol (CHE) China Peking Univ \n332586 Swiss Fed Inst Technol (CHE) China Southeast Univ \n332594 Swiss Fed Inst Technol (CHE) China Tongji Univ \n332613 Swiss Fed Inst Technol (CHE) China Tsinghua Univ \n332615 Swiss Fed Inst Technol (CHE) China Harbin Inst Technol \n\n Country_Type_ch Eurovoc_Class_ch ISO3_ch \n102 China China CHN \\\n103 China China CHN \n105 China China CHN \n146 China China CHN \n148 China China CHN \n... ... ... ... \n332571 China China CHN \n332586 China China CHN \n332594 China China CHN \n332613 China China CHN \n332615 China China CHN \n\n Institution_harm_label_ch Domain_English \n102 Univ Sci & Technol China (CHN) Natural Sciences \\\n103 Univ Sci & Technol China (CHN) Natural Sciences \n105 Univ Sci & Technol China (CHN) Natural Sciences \n146 Peking Univ (CHN) Natural Sciences \n148 Peking Univ (CHN) Natural Sciences \n... ... ... \n332571 Peking Univ (CHN) Applied Sciences \n332586 Southeast Univ (CHN) Natural Sciences \n332594 Tongji Univ (CHN) Applied Sciences \n332613 Tsinghua Univ (CHN) Health Sciences \n332615 Harbin Inst Technol (CHN) Applied Sciences \n\n Field_English \n102 Physics & Astronomy \\\n103 Physics & Astronomy \n105 Physics & Astronomy \n146 Physics & Astronomy \n148 Physics & Astronomy \n... ... \n332571 Information & Communication Technologies \n332586 Physics & Astronomy \n332594 Information & Communication Technologies \n332613 Psychology & Cognitive Sciences \n332615 Information & Communication Technologies \n\n SubField_English \n102 Astronomy & Astrophysics \n103 Astronomy & Astrophysics \n105 Astronomy & Astrophysics \n146 Nuclear & Particle Physics \n148 Nuclear & Particle Physics \n... ... \n332571 Artificial Intelligence & Image Processing \n332586 Optics \n332594 Artificial Intelligence & Image Processing \n332613 Experimental Psychology \n332615 Artificial Intelligence & Image Processing \n\n[19776 rows x 16 columns]", "text/html": "
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UT (Unique WOS ID)Country_euInstitution_harm_euCountry_Type_euEurovoc_Class_euISO3_euInstitution_harm_label_euCountry_chInstitution_harm_chCountry_Type_chEurovoc_Class_chISO3_chInstitution_harm_label_chDomain_EnglishField_EnglishSubField_English
102WOS:000296783400024United KingdomUniv NottinghamNon-EU associateWestern EuropeGBRUniv Nottingham (GBR)ChinaUniv Sci & Technol ChinaChinaChinaCHNUniv Sci & Technol China (CHN)Natural SciencesPhysics & AstronomyAstronomy & Astrophysics
103WOS:000296783400024United KingdomCardiff UnivNon-EU associateWestern EuropeGBRCardiff Univ (GBR)ChinaUniv Sci & Technol ChinaChinaChinaCHNUniv Sci & Technol China (CHN)Natural SciencesPhysics & AstronomyAstronomy & Astrophysics
105WOS:000296783400024United KingdomUniv EdinburghNon-EU associateWestern EuropeGBRUniv Edinburgh (GBR)ChinaUniv Sci & Technol ChinaChinaChinaCHNUniv Sci & Technol China (CHN)Natural SciencesPhysics & AstronomyAstronomy & Astrophysics
146WOS:000298320400037United KingdomImperial Coll LondonNon-EU associateWestern EuropeGBRImperial Coll London (GBR)ChinaPeking UnivChinaChinaCHNPeking Univ (CHN)Natural SciencesPhysics & AstronomyNuclear & Particle Physics
148WOS:000298320400037United KingdomBrunel Univ LondonNon-EU associateWestern EuropeGBRBrunel Univ London (GBR)ChinaPeking UnivChinaChinaCHNPeking Univ (CHN)Natural SciencesPhysics & AstronomyNuclear & Particle Physics
...................................................
332571WOS:000897707600137SwitzerlandSwiss Fed Inst TechnolNon-EU associateWestern EuropeCHESwiss Fed Inst Technol (CHE)ChinaPeking UnivChinaChinaCHNPeking Univ (CHN)Applied SciencesInformation & Communication TechnologiesArtificial Intelligence & Image Processing
332586WOS:000902112600001SwitzerlandSwiss Fed Inst TechnolNon-EU associateWestern EuropeCHESwiss Fed Inst Technol (CHE)ChinaSoutheast UnivChinaChinaCHNSoutheast Univ (CHN)Natural SciencesPhysics & AstronomyOptics
332594WOS:000906995300001SwitzerlandSwiss Fed Inst TechnolNon-EU associateWestern EuropeCHESwiss Fed Inst Technol (CHE)ChinaTongji UnivChinaChinaCHNTongji Univ (CHN)Applied SciencesInformation & Communication TechnologiesArtificial Intelligence & Image Processing
332613WOS:000922928206021SwitzerlandSwiss Fed Inst TechnolNon-EU associateWestern EuropeCHESwiss Fed Inst Technol (CHE)ChinaTsinghua UnivChinaChinaCHNTsinghua Univ (CHN)Health SciencesPsychology & Cognitive SciencesExperimental Psychology
332615WOS:000923379400006SwitzerlandSwiss Fed Inst TechnolNon-EU associateWestern EuropeCHESwiss Fed Inst Technol (CHE)ChinaHarbin Inst TechnolChinaChinaCHNHarbin Inst Technol (CHN)Applied SciencesInformation & Communication TechnologiesArtificial Intelligence & Image Processing
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19776 rows × 16 columns

\n
" }, - "execution_count": 64, + "execution_count": 246, "metadata": {}, "output_type": "execute_result" } @@ -293913,7 +295242,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 247, "id": "7bd7d149", "metadata": {}, "outputs": [ @@ -294659,7 +295988,7 @@ "type": "heatmap", "xaxis": "x", "yaxis": "y", - "hovertemplate": "Institute (CH): %{x}
Institute (EU): %{y}
Co-publication: %{z}" + "hovertemplate": "Institute (CN): %{x}
Institute (EU): %{y}
Co-publication: %{z}" } ], "layout": { @@ -295488,7 +296817,7 @@ "scaleanchor": "y", "constrain": "domain", "title": { - "text": "Institute (CH)" + "text": "Institute (CN)" }, "showgrid": false, "tickangle": -45, @@ -295576,7 +296905,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -295601,7 +296930,7 @@ "mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", "data = np.where(mask,inst_co_occur,inst_co_occur)\n", "fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Most visible institutions (top {TOPN} within EU-28)\"\n", " )\n", @@ -295627,7 +296956,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 248, "outputs": [], "source": [ "# fig.write_html(f\"plot_html/overall_inst_collab_eu28.html\",config= dict(displayModeBar = False, responsive = True))\n", @@ -295640,7 +296969,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 249, "outputs": [ { "data": { @@ -295648,7 +296977,7 @@ "data": [ { "coloraxis": "coloraxis", - "hovertemplate": "Institute (CH): %{x}
Institute (EU): %{y}
Co-publication: %{z}", + "hovertemplate": "Institute (CN): %{x}
Institute (EU): %{y}
Co-publication: %{z}", "name": "0", "texttemplate": "%{z}", "x": [ @@ -297277,7 +298606,7 @@ "tickangle": 45, "ticks": "outside", "title": { - "text": "Institute (CH)" + "text": "Institute (CN)" }, "tickfont": { "size": 16 @@ -297312,7 +298641,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -297347,7 +298676,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 250, "outputs": [], "source": [ "id_subset = wos[wos[\"Domain_English\"]=='Applied Sciences'][record_col].unique()\n", @@ -297375,14 +298704,14 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 251, "outputs": [ { "data": { "text/plain": " Publication Year Field_English UT (Unique WOS ID) \n0 2011 Agriculture, Fisheries & Forestry 9.0 \\\n1 2012 Agriculture, Fisheries & Forestry 18.0 \n2 2013 Agriculture, Fisheries & Forestry 15.0 \n3 2014 Agriculture, Fisheries & Forestry 26.0 \n4 2015 Agriculture, Fisheries & Forestry 12.0 \n.. ... ... ... \n79 2018 Social Sciences 0.0 \n80 2019 Social Sciences 0.0 \n81 2020 Social Sciences 0.0 \n82 2021 Social Sciences 0.0 \n83 2022 Social Sciences 0.0 \n\n Domain_English Publication Year_relative_growth \n0 Applied Sciences 2011 \\\n1 Applied Sciences 2011 \n2 Applied Sciences 2011 \n3 Applied Sciences 2011 \n4 Applied Sciences 2011 \n.. ... ... \n79 Applied Sciences 2017 \n80 Applied Sciences 2017 \n81 Applied Sciences 2017 \n82 Applied Sciences 2017 \n83 Applied Sciences 2017 \n\n UT (Unique WOS ID)_relative_growth Domain_English_relative_growth \n0 0.000000 Applied Sciences \\\n1 1.000000 Applied Sciences \n2 0.666667 Applied Sciences \n3 1.888889 Applied Sciences \n4 0.333333 Applied Sciences \n.. ... ... \n79 -1.000000 Applied Sciences \n80 -1.000000 Applied Sciences \n81 -1.000000 Applied Sciences \n82 -1.000000 Applied Sciences \n83 -1.000000 Applied Sciences \n\n UT (Unique WOS ID)_cumsum \n0 9.0 \n1 27.0 \n2 42.0 \n3 68.0 \n4 80.0 \n.. ... \n79 1.0 \n80 1.0 \n81 1.0 \n82 1.0 \n83 1.0 \n\n[84 rows x 8 columns]", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication YearField_EnglishUT (Unique WOS ID)Domain_EnglishPublication Year_relative_growthUT (Unique WOS ID)_relative_growthDomain_English_relative_growthUT (Unique WOS ID)_cumsum
02011Agriculture, Fisheries & Forestry9.0Applied Sciences20110.000000Applied Sciences9.0
12012Agriculture, Fisheries & Forestry18.0Applied Sciences20111.000000Applied Sciences27.0
22013Agriculture, Fisheries & Forestry15.0Applied Sciences20110.666667Applied Sciences42.0
32014Agriculture, Fisheries & Forestry26.0Applied Sciences20111.888889Applied Sciences68.0
42015Agriculture, Fisheries & Forestry12.0Applied Sciences20110.333333Applied Sciences80.0
...........................
792018Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
802019Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
812020Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
822021Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
832022Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
\n

84 rows × 8 columns

\n
" }, - "execution_count": 69, + "execution_count": 251, "metadata": {}, "output_type": "execute_result" } @@ -297396,7 +298725,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 252, "outputs": [], "source": [ "group = ['Publication Year',\"Domain_English\",'Field_English']\n", @@ -297424,14 +298753,14 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 253, "outputs": [ { "data": { "text/plain": " Publication Year Domain_English Field_English \n0 2011 Applied Sciences Agriculture, Fisheries & Forestry \\\n1 2011 Applied Sciences Biology \n2 2011 Applied Sciences Biomedical Research \n3 2011 Applied Sciences Built Environment & Design \n4 2011 Applied Sciences Chemistry \n... ... ... ... \n1375 2022 Natural Sciences Philosophy & Theology \n1376 2022 Natural Sciences Physics & Astronomy \n1377 2022 Natural Sciences Psychology & Cognitive Sciences \n1378 2022 Natural Sciences Public Health & Health Services \n1379 2022 Natural Sciences Social Sciences \n\n UT (Unique WOS ID) \n0 9.0 \n1 0.0 \n2 0.0 \n3 6.0 \n4 0.0 \n... ... \n1375 0.0 \n1376 596.0 \n1377 0.0 \n1378 0.0 \n1379 0.0 \n\n[1380 rows x 4 columns]", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication YearDomain_EnglishField_EnglishUT (Unique WOS ID)
02011Applied SciencesAgriculture, Fisheries & Forestry9.0
12011Applied SciencesBiology0.0
22011Applied SciencesBiomedical Research0.0
32011Applied SciencesBuilt Environment & Design6.0
42011Applied SciencesChemistry0.0
...............
13752022Natural SciencesPhilosophy & Theology0.0
13762022Natural SciencesPhysics & Astronomy596.0
13772022Natural SciencesPsychology & Cognitive Sciences0.0
13782022Natural SciencesPublic Health & Health Services0.0
13792022Natural SciencesSocial Sciences0.0
\n

1380 rows × 4 columns

\n
" }, - "execution_count": 71, + "execution_count": 253, "metadata": {}, "output_type": "execute_result" } @@ -297453,14 +298782,14 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 254, "outputs": [ { "data": { "text/plain": " Field_English UT (Unique WOS ID)\n5 Information & Communication Technologies 15648\n4 Engineering 9232\n3 Enabling & Strategic Technologies 3940\n0 Agriculture, Fisheries & Forestry 612\n1 Built Environment & Design 537\n2 Economics & Business 15\n6 Social Sciences 1", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Field_EnglishUT (Unique WOS ID)
5Information & Communication Technologies15648
4Engineering9232
3Enabling & Strategic Technologies3940
0Agriculture, Fisheries & Forestry612
1Built Environment & Design537
2Economics & Business15
6Social Sciences1
\n
" }, - "execution_count": 72, + "execution_count": 254, "metadata": {}, "output_type": "execute_result" } @@ -297485,14 +298814,14 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 255, "outputs": [ { "data": { "text/plain": " Publication Year Domain_English \n0 2011 Applied Sciences \\\n1 2012 Applied Sciences \n2 2013 Applied Sciences \n3 2014 Applied Sciences \n4 2015 Applied Sciences \n5 2016 Applied Sciences \n6 2017 Applied Sciences \n7 2018 Applied Sciences \n8 2019 Applied Sciences \n9 2020 Applied Sciences \n10 2021 Applied Sciences \n11 2022 Applied Sciences \n12 2011 Applied Sciences \n13 2012 Applied Sciences \n14 2013 Applied Sciences \n15 2014 Applied Sciences \n16 2015 Applied Sciences \n17 2016 Applied Sciences \n18 2017 Applied Sciences \n19 2018 Applied Sciences \n20 2019 Applied Sciences \n21 2020 Applied Sciences \n22 2021 Applied Sciences \n23 2022 Applied Sciences \n24 2011 Applied Sciences \n25 2012 Applied Sciences \n26 2013 Applied Sciences \n27 2014 Applied Sciences \n28 2015 Applied Sciences \n29 2016 Applied Sciences \n30 2017 Applied Sciences \n31 2018 Applied Sciences \n32 2019 Applied Sciences \n33 2020 Applied Sciences \n34 2021 Applied Sciences \n35 2022 Applied Sciences \n36 2011 Applied Sciences \n37 2012 Applied Sciences \n38 2013 Applied Sciences \n39 2014 Applied Sciences \n40 2015 Applied Sciences \n41 2016 Applied Sciences \n42 2017 Applied Sciences \n43 2018 Applied Sciences \n44 2019 Applied Sciences \n45 2020 Applied Sciences \n46 2021 Applied Sciences \n47 2022 Applied Sciences \n48 2011 Applied Sciences \n49 2012 Applied Sciences \n50 2013 Applied Sciences \n51 2014 Applied Sciences \n52 2015 Applied Sciences \n53 2016 Applied Sciences \n54 2017 Applied Sciences \n55 2018 Applied Sciences \n56 2019 Applied Sciences \n57 2020 Applied Sciences \n58 2021 Applied Sciences \n59 2022 Applied Sciences \n\n Field_English UT (Unique WOS ID) \n0 Agriculture, Fisheries & Forestry 9.0 \\\n1 Agriculture, Fisheries & Forestry 18.0 \n2 Agriculture, Fisheries & Forestry 15.0 \n3 Agriculture, Fisheries & Forestry 26.0 \n4 Agriculture, Fisheries & Forestry 12.0 \n5 Agriculture, Fisheries & Forestry 24.0 \n6 Agriculture, Fisheries & Forestry 39.0 \n7 Agriculture, Fisheries & Forestry 46.0 \n8 Agriculture, Fisheries & Forestry 73.0 \n9 Agriculture, Fisheries & Forestry 105.0 \n10 Agriculture, Fisheries & Forestry 118.0 \n11 Agriculture, Fisheries & Forestry 127.0 \n12 Built Environment & Design 6.0 \n13 Built Environment & Design 8.0 \n14 Built Environment & Design 1.0 \n15 Built Environment & Design 17.0 \n16 Built Environment & Design 18.0 \n17 Built Environment & Design 13.0 \n18 Built Environment & Design 24.0 \n19 Built Environment & Design 41.0 \n20 Built Environment & Design 59.0 \n21 Built Environment & Design 84.0 \n22 Built Environment & Design 116.0 \n23 Built Environment & Design 150.0 \n24 Enabling & Strategic Technologies 53.0 \n25 Enabling & Strategic Technologies 61.0 \n26 Enabling & Strategic Technologies 80.0 \n27 Enabling & Strategic Technologies 115.0 \n28 Enabling & Strategic Technologies 117.0 \n29 Enabling & Strategic Technologies 161.0 \n30 Enabling & Strategic Technologies 213.0 \n31 Enabling & Strategic Technologies 330.0 \n32 Enabling & Strategic Technologies 468.0 \n33 Enabling & Strategic Technologies 577.0 \n34 Enabling & Strategic Technologies 815.0 \n35 Enabling & Strategic Technologies 950.0 \n36 Engineering 166.0 \n37 Engineering 181.0 \n38 Engineering 238.0 \n39 Engineering 318.0 \n40 Engineering 381.0 \n41 Engineering 481.0 \n42 Engineering 587.0 \n43 Engineering 767.0 \n44 Engineering 1059.0 \n45 Engineering 1379.0 \n46 Engineering 1626.0 \n47 Engineering 2049.0 \n48 Information & Communication Technologies 256.0 \n49 Information & Communication Technologies 325.0 \n50 Information & Communication Technologies 404.0 \n51 Information & Communication Technologies 554.0 \n52 Information & Communication Technologies 673.0 \n53 Information & Communication Technologies 855.0 \n54 Information & Communication Technologies 1055.0 \n55 Information & Communication Technologies 1622.0 \n56 Information & Communication Technologies 2068.0 \n57 Information & Communication Technologies 2296.0 \n58 Information & Communication Technologies 2617.0 \n59 Information & Communication Technologies 2923.0 \n\n Publication Year_relative_growth UT (Unique WOS ID)_relative_growth \n0 2011 0.000000 \\\n1 2011 1.000000 \n2 2011 0.666667 \n3 2011 1.888889 \n4 2011 0.333333 \n5 2011 1.666667 \n6 2011 3.333333 \n7 2011 4.111111 \n8 2011 7.111111 \n9 2011 10.666667 \n10 2011 12.111111 \n11 2011 13.111111 \n12 2011 0.000000 \n13 2011 0.333333 \n14 2011 -0.833333 \n15 2011 1.833333 \n16 2011 2.000000 \n17 2011 1.166667 \n18 2011 3.000000 \n19 2011 5.833333 \n20 2011 8.833333 \n21 2011 13.000000 \n22 2011 18.333333 \n23 2011 24.000000 \n24 2011 0.000000 \n25 2011 0.150943 \n26 2011 0.509434 \n27 2011 1.169811 \n28 2011 1.207547 \n29 2011 2.037736 \n30 2011 3.018868 \n31 2011 5.226415 \n32 2011 7.830189 \n33 2011 9.886792 \n34 2011 14.377358 \n35 2011 16.924528 \n36 2011 0.000000 \n37 2011 0.090361 \n38 2011 0.433735 \n39 2011 0.915663 \n40 2011 1.295181 \n41 2011 1.897590 \n42 2011 2.536145 \n43 2011 3.620482 \n44 2011 5.379518 \n45 2011 7.307229 \n46 2011 8.795181 \n47 2011 11.343373 \n48 2011 0.000000 \n49 2011 0.269531 \n50 2011 0.578125 \n51 2011 1.164062 \n52 2011 1.628906 \n53 2011 2.339844 \n54 2011 3.121094 \n55 2011 5.335938 \n56 2011 7.078125 \n57 2011 7.968750 \n58 2011 9.222656 \n59 2011 10.417969 \n\n UT (Unique WOS ID)_cumsum \n0 9.0 \n1 27.0 \n2 42.0 \n3 68.0 \n4 80.0 \n5 104.0 \n6 143.0 \n7 189.0 \n8 262.0 \n9 367.0 \n10 485.0 \n11 612.0 \n12 6.0 \n13 14.0 \n14 15.0 \n15 32.0 \n16 50.0 \n17 63.0 \n18 87.0 \n19 128.0 \n20 187.0 \n21 271.0 \n22 387.0 \n23 537.0 \n24 53.0 \n25 114.0 \n26 194.0 \n27 309.0 \n28 426.0 \n29 587.0 \n30 800.0 \n31 1130.0 \n32 1598.0 \n33 2175.0 \n34 2990.0 \n35 3940.0 \n36 166.0 \n37 347.0 \n38 585.0 \n39 903.0 \n40 1284.0 \n41 1765.0 \n42 2352.0 \n43 3119.0 \n44 4178.0 \n45 5557.0 \n46 7183.0 \n47 9232.0 \n48 256.0 \n49 581.0 \n50 985.0 \n51 1539.0 \n52 2212.0 \n53 3067.0 \n54 4122.0 \n55 5744.0 \n56 7812.0 \n57 10108.0 \n58 12725.0 \n59 15648.0 ", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication YearDomain_EnglishField_EnglishUT (Unique WOS ID)Publication Year_relative_growthUT (Unique WOS ID)_relative_growthUT (Unique WOS ID)_cumsum
02011Applied SciencesAgriculture, Fisheries & Forestry9.020110.0000009.0
12012Applied SciencesAgriculture, Fisheries & Forestry18.020111.00000027.0
22013Applied SciencesAgriculture, Fisheries & Forestry15.020110.66666742.0
32014Applied SciencesAgriculture, Fisheries & Forestry26.020111.88888968.0
42015Applied SciencesAgriculture, Fisheries & Forestry12.020110.33333380.0
52016Applied SciencesAgriculture, Fisheries & Forestry24.020111.666667104.0
62017Applied SciencesAgriculture, Fisheries & Forestry39.020113.333333143.0
72018Applied SciencesAgriculture, Fisheries & Forestry46.020114.111111189.0
82019Applied SciencesAgriculture, Fisheries & Forestry73.020117.111111262.0
92020Applied SciencesAgriculture, Fisheries & Forestry105.0201110.666667367.0
102021Applied SciencesAgriculture, Fisheries & Forestry118.0201112.111111485.0
112022Applied SciencesAgriculture, Fisheries & Forestry127.0201113.111111612.0
122011Applied SciencesBuilt Environment & Design6.020110.0000006.0
132012Applied SciencesBuilt Environment & Design8.020110.33333314.0
142013Applied SciencesBuilt Environment & Design1.02011-0.83333315.0
152014Applied SciencesBuilt Environment & Design17.020111.83333332.0
162015Applied SciencesBuilt Environment & Design18.020112.00000050.0
172016Applied SciencesBuilt Environment & Design13.020111.16666763.0
182017Applied SciencesBuilt Environment & Design24.020113.00000087.0
192018Applied SciencesBuilt Environment & Design41.020115.833333128.0
202019Applied SciencesBuilt Environment & Design59.020118.833333187.0
212020Applied SciencesBuilt Environment & Design84.0201113.000000271.0
222021Applied SciencesBuilt Environment & Design116.0201118.333333387.0
232022Applied SciencesBuilt Environment & Design150.0201124.000000537.0
242011Applied SciencesEnabling & Strategic Technologies53.020110.00000053.0
252012Applied SciencesEnabling & Strategic Technologies61.020110.150943114.0
262013Applied SciencesEnabling & Strategic Technologies80.020110.509434194.0
272014Applied SciencesEnabling & Strategic Technologies115.020111.169811309.0
282015Applied SciencesEnabling & Strategic Technologies117.020111.207547426.0
292016Applied SciencesEnabling & Strategic Technologies161.020112.037736587.0
302017Applied SciencesEnabling & Strategic Technologies213.020113.018868800.0
312018Applied SciencesEnabling & Strategic Technologies330.020115.2264151130.0
322019Applied SciencesEnabling & Strategic Technologies468.020117.8301891598.0
332020Applied SciencesEnabling & Strategic Technologies577.020119.8867922175.0
342021Applied SciencesEnabling & Strategic Technologies815.0201114.3773582990.0
352022Applied SciencesEnabling & Strategic Technologies950.0201116.9245283940.0
362011Applied SciencesEngineering166.020110.000000166.0
372012Applied SciencesEngineering181.020110.090361347.0
382013Applied SciencesEngineering238.020110.433735585.0
392014Applied SciencesEngineering318.020110.915663903.0
402015Applied SciencesEngineering381.020111.2951811284.0
412016Applied SciencesEngineering481.020111.8975901765.0
422017Applied SciencesEngineering587.020112.5361452352.0
432018Applied SciencesEngineering767.020113.6204823119.0
442019Applied SciencesEngineering1059.020115.3795184178.0
452020Applied SciencesEngineering1379.020117.3072295557.0
462021Applied SciencesEngineering1626.020118.7951817183.0
472022Applied SciencesEngineering2049.0201111.3433739232.0
482011Applied SciencesInformation & Communication Technologies256.020110.000000256.0
492012Applied SciencesInformation & Communication Technologies325.020110.269531581.0
502013Applied SciencesInformation & Communication Technologies404.020110.578125985.0
512014Applied SciencesInformation & Communication Technologies554.020111.1640621539.0
522015Applied SciencesInformation & Communication Technologies673.020111.6289062212.0
532016Applied SciencesInformation & Communication Technologies855.020112.3398443067.0
542017Applied SciencesInformation & Communication Technologies1055.020113.1210944122.0
552018Applied SciencesInformation & Communication Technologies1622.020115.3359385744.0
562019Applied SciencesInformation & Communication Technologies2068.020117.0781257812.0
572020Applied SciencesInformation & Communication Technologies2296.020117.96875010108.0
582021Applied SciencesInformation & Communication Technologies2617.020119.22265612725.0
592022Applied SciencesInformation & Communication Technologies2923.0201110.41796915648.0
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" }, - "execution_count": 73, + "execution_count": 255, "metadata": {}, "output_type": "execute_result" } @@ -297506,14 +298835,14 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 256, "outputs": [ { "data": { "text/plain": " Field_English UT (Unique WOS ID)\n0 Agriculture, Fisheries & Forestry 612\n1 Built Environment & Design 537\n2 Economics & Business 15\n3 Enabling & Strategic Technologies 3940\n4 Engineering 9232\n5 Information & Communication Technologies 15648\n6 Social Sciences 1", "text/html": "
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Field_EnglishUT (Unique WOS ID)
0Agriculture, Fisheries & Forestry612
1Built Environment & Design537
2Economics & Business15
3Enabling & Strategic Technologies3940
4Engineering9232
5Information & Communication Technologies15648
6Social Sciences1
\n
" }, - "execution_count": 74, + "execution_count": 256, "metadata": {}, "output_type": "execute_result" } @@ -297527,7 +298856,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 256, "outputs": [], "source": [], "metadata": { @@ -297536,7 +298865,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 257, "outputs": [], "source": [ "%%capture\n", @@ -298231,7 +299560,7 @@ " data = np.where(mask,inst_co_occur,inst_co_occur)\n", "\n", " fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Top {TOPN} institutes within Europe ({cat})\"\n", " )\n", @@ -298284,7 +299613,7 @@ " mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", " data = np.where(mask,inst_co_occur,inst_co_occur)\n", " fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Top {TOPN} institutes within EU-28 ({cat})\"\n", " )\n", @@ -298325,7 +299654,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 258, "outputs": [], "source": [ "# Drill down to subfield" @@ -298336,7 +299665,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 259, "outputs": [], "source": [ "%%capture\n", @@ -299040,7 +300369,7 @@ " data = np.where(mask,inst_co_occur,inst_co_occur)\n", "\n", " fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Top {TOPN} institutes within Europe ({cat2})\"\n", " )\n", @@ -299095,7 +300424,7 @@ "\n", "\n", " fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Top {TOPN} institutes within EU-28 ({cat2})\"\n", " )\n", @@ -299136,7 +300465,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 260, "outputs": [], "source": [ "%%capture\n", @@ -299639,7 +300968,7 @@ " data = np.where(mask,inst_co_occur,inst_co_occur)\n", "\n", " fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Top {TOPN} institutes within Europe ({t})\"\n", " )\n", @@ -299692,7 +301021,7 @@ " mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", " data = np.where(mask,inst_co_occur,inst_co_occur)\n", " fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Top {TOPN} institutes within EU-28 ({t})\"\n", " )\n", @@ -299733,7 +301062,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 261, "outputs": [], "source": [ "%%capture\n", @@ -299742,15 +301071,15 @@ "\n", "# Adding trending subfields\n", "# Applied sciences\n", - "trending_topics = [\"Distributed Computing\", \"Nanoscience & Nanotechnology\", \"Building & Construction\"]\n", + "trending_topics = [\"Distributed Computing\", \"Nanoscience & Nanotechnology\", \"Building & Construction\", \"Nuclear & Particles Physics\", \"Analytical Chemistry\"]\n", "\n", "# Natural Sciences -> Analytical chemistry\n", "\n", - "trending_topics = [\"Distributed Computing\", \"Nanoscience & Nanotechnology\", \"Building & Construction\",\"Analytical Chemistry\"]\n", + "trending_topics = [\"Distributed Computing\", \"Nanoscience & Nanotechnology\", \"Building & Construction\",\"Analytical Chemistry\",\"Nuclear & Particle Physics\"]\n", "for t in trending_topics:\n", " os.makedirs(rf'plot_html/PPT_plots/trending_topics/{t}',exist_ok=True)\n", "\n", - " if t == \"Analytical Chemistry\":\n", + " if t == \"Analytical Chemistry\" or t == \"Nuclear & Particle Physics\":\n", " subset = \"Natural Sciences\"\n", " else:\n", " subset = \"Applied Sciences\"\n", @@ -300246,7 +301575,7 @@ " data = np.where(mask,inst_co_occur,inst_co_occur)\n", "\n", " fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Top {TOPN} institutes within Europe ({t})\"\n", " )\n", @@ -300299,7 +301628,7 @@ " mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", " data = np.where(mask,inst_co_occur,inst_co_occur)\n", " fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", + " labels=dict(x=\"Institute (CN)\", y=\"Institute (EU)\", color=\"Co-publication\"),text_auto=True,\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Top {TOPN} institutes within EU-28 ({t})\"\n", " )\n", @@ -300340,7 +301669,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 69, "outputs": [], "source": [ "# Adding emphasized countries\n", @@ -300356,7 +301685,7 @@ "#Economic Social Sciences :France\n", "\n", "\n", - "scope_dict = {\"General\":[\"Croatia\",\"Cyprus\",\"Luxembourg\"],\n", + "scope_dict = {\"General\":[\"Croatia\",\"Cyprus\",\"Luxembourg\",\"Poland\"],\n", " \"Applied Sciences\":[\"Hungary\",\"Poland\"],\n", " \"Natural Sciences\":[\"Ireland\"],\n", " \"Health Sciences\":[\"Austria\",\"Czech Republic\",\"Ireland\",\"Poland\",\"Portugal\"],\n", @@ -300384,6 +301713,8 @@ " .groupby(groups, as_index=False)[record_col]\n", " .nunique()\n", " .sort_values(ascending=False, by=record_col))\n", + "\n", + "\n", " data[\"percent\"] = data[record_col]/data[record_col].sum()*100\n", " data[groups] = data[groups].applymap(replace_nth)\n", " fig = px.sunburst(data, path=groups, values=record_col,\n", @@ -300408,16 +301739,36 @@ " wos_univ_collabs = wos_univ_collabs.merge(wos[[record_col,\"Domain_English\"]],on=record_col)\n", "\n", "\n", + "\n", " wos_univ_ch = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"China\"]\n", " wos_univ_eu = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]!=\"China\"]\n", "\n", " wos_univ_eu_strict = wos_univ_collabs[wos_univ_collabs[\"Country\"]==c]\n", "\n", - " data_eu_strict = (wos_univ_eu_strict.groupby([\"Country\",\"Institution_harm\",\"Domain_English\"], as_index=False)[record_col].nunique()\n", - " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", "\n", - " data_ch = (wos_univ_ch.groupby([\"Country\",\"Institution_harm\",\"Domain_English\"], as_index=False)[record_col].nunique()\n", - " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", + " data_eu_strict_inst_top = (wos_univ_eu_strict\n", + " .groupby([\"Country\",\"Institution_harm\"], as_index=False)[record_col]\n", + " .nunique()\n", + " .sort_values(by=record_col,ascending=False)\n", + " .head(TOPN)\n", + " .copy())[\"Institution_harm\"].unique()\n", + "\n", + " data_ch_strict_inst_top = (wos_univ_ch\n", + " .groupby([\"Country\",\"Institution_harm\"], as_index=False)[record_col]\n", + " .nunique()\n", + " .sort_values(by=record_col,ascending=False)\n", + " .head(TOPN)\n", + " .copy())[\"Institution_harm\"].unique()\n", + "\n", + " data_eu_strict = (wos_univ_eu_strict[wos_univ_eu_strict[\"Institution_harm\"].isin(data_eu_strict_inst_top)]\n", + " .groupby([\"Country\",\"Institution_harm\",\"Domain_English\"], as_index=False)[record_col]\n", + " .nunique().sort_values(by=record_col,ascending=False).copy())\n", + "\n", + " data_ch = (wos_univ_ch[wos_univ_ch[\"Institution_harm\"].isin(data_ch_strict_inst_top)]\n", + " .groupby([\"Country\",\"Institution_harm\",\"Domain_English\"], as_index=False)[record_col]\n", + " .nunique()\n", + " .sort_values(by=record_col,ascending=False)\n", + " .copy())\n", "\n", "\n", " for data,c_scope, y_lab, in zip(\n", @@ -300489,7 +301840,7 @@ " data = np.where(mask,inst_co_occur,inst_co_occur)\n", "\n", " fig = px.imshow(data,\n", - " labels=dict(x=\"Institute (CH)\", y=f\"Institute ({c})\", color=\"Co-publication\"),text_auto=True,\n", + " labels=dict(x=\"Institute (CN)\", y=f\"Institute ({c})\", color=\"Co-publication\"),text_auto=True,\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Top {TOPN} institutes ({t})\"\n", " )\n", @@ -300530,7 +301881,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 263, "outputs": [ { "name": "stdout", @@ -300698,14 +302049,14 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 264, "outputs": [ { "data": { - "text/plain": " Country Institution_harm Country_Type \n59 China Fudan Univ China \\\n182 China Univ Hong Kong China \n169 China Sun Yat Sen Univ China \n146 China Shanghai Jiao Tong Univ China \n190 China Wuhan Univ China \n34 China Chinese Ctr Dis Control & Prevent China \n39 China Chinese Univ Hong Kong China \n175 China Tianjin Med Univ China \n20 China Cent South Univ China \n119 China Peking Univ China \n\n UT (Unique WOS ID) \n59 41 \n182 35 \n169 29 \n146 28 \n190 27 \n34 26 \n39 24 \n175 22 \n20 17 \n119 15 ", - "text/html": "
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CountryInstitution_harmCountry_TypeUT (Unique WOS ID)
59ChinaFudan UnivChina41
182ChinaUniv Hong KongChina35
169ChinaSun Yat Sen UnivChina29
146ChinaShanghai Jiao Tong UnivChina28
190ChinaWuhan UnivChina27
34ChinaChinese Ctr Dis Control & PreventChina26
39ChinaChinese Univ Hong KongChina24
175ChinaTianjin Med UnivChina22
20ChinaCent South UnivChina17
119ChinaPeking UnivChina15
\n
" + "text/plain": " Country Institution_harm Domain_English \n59 China Fudan Univ Health Sciences \\\n182 China Univ Hong Kong Health Sciences \n169 China Sun Yat Sen Univ Health Sciences \n146 China Shanghai Jiao Tong Univ Health Sciences \n190 China Wuhan Univ Health Sciences \n34 China Chinese Ctr Dis Control & Prevent Health Sciences \n39 China Chinese Univ Hong Kong Health Sciences \n175 China Tianjin Med Univ Health Sciences \n20 China Cent South Univ Health Sciences \n119 China Peking Univ Health Sciences \n\n UT (Unique WOS ID) \n59 41 \n182 35 \n169 29 \n146 28 \n190 27 \n34 26 \n39 24 \n175 22 \n20 17 \n119 15 ", + "text/html": "
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CountryInstitution_harmDomain_EnglishUT (Unique WOS ID)
59ChinaFudan UnivHealth Sciences41
182ChinaUniv Hong KongHealth Sciences35
169ChinaSun Yat Sen UnivHealth Sciences29
146ChinaShanghai Jiao Tong UnivHealth Sciences28
190ChinaWuhan UnivHealth Sciences27
34ChinaChinese Ctr Dis Control & PreventHealth Sciences26
39ChinaChinese Univ Hong KongHealth Sciences24
175ChinaTianjin Med UnivHealth Sciences22
20ChinaCent South UnivHealth Sciences17
119ChinaPeking UnivHealth Sciences15
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UT (Unique WOS ID)InstitutionCountryInstitution_harmmerge_iter
0WOS:000208837000001Univ LiegeBelgiumUniv Liege0
1WOS:000208837000001Northwestern Polytech UnivChinaNorthwestern Polytech Univ0
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3WOS:000208863600013Zhejiang UnivChinaZhejiang Univ0
4WOS:000208863600013Delft Univ TechnolNetherlandsDelft Univ Technol0
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" }, - "execution_count": 86, + "execution_count": 265, "metadata": {}, "output_type": "execute_result" } diff --git a/WOS/wos_processing_pipeline.ipynb b/WOS/wos_processing_pipeline.ipynb index 53d998b..e548e55 100644 --- a/WOS/wos_processing_pipeline.ipynb +++ b/WOS/wos_processing_pipeline.ipynb @@ -56,8 +56,8 @@ "outputs": [ { "data": { - "text/plain": " Publication Type Authors \n20209 J Shuang, K; Gu, MY; Li, R; Loo, J; Su, S \\\n9308 J Kuo, CY; Schaarschmidt, A; Cui, YD; Asfour, T;... \n26162 J Jin, BT; Zhou, ZH; Zou, J \n9129 J Sun, QM; Zhang, X; Banerjee, S; Bao, P; Barbry... \n24590 J Jiang, XH; Zhang, WY; Fernie, AR; Wen, WW \n... ... ... \n30650 J Guan, ZT; Zhang, Y; Si, GL; Zhou, ZY; Wu, J; M... \n31337 J Liu, H; Zhang, Y; Yang, T \n4273 J Zhou, YL; Chang, FJ; Chen, H; Li, H \n6761 C Li, Y; Tao, JH; Schuller, B; Shan, SG; Jiang, ... \n41900 J Shang, RH; Kong, JR; Zhang, WT; Feng, J; Jiao,... \n\n Book Authors Book Editors \n20209 NaN NaN \\\n9308 NaN NaN \n26162 NaN NaN \n9129 NaN NaN \n24590 NaN NaN \n... ... ... \n30650 NaN NaN \n31337 NaN NaN \n4273 NaN NaN \n6761 NaN Tan, T; Li, X; Chen, X; Zhou, J; Yang, J; Chen... \n41900 NaN NaN \n\n Book Group Authors Author Full Names \n20209 NaN Shuang, Kai; Gu, Mengyu; Li, Rui; Loo, Jonatha... \\\n9308 NaN Kuo, Cheng-Yu; Schaarschmidt, Andreas; Cui, Yu... \n26162 NaN Jin, Bangti; Zhou, Zehui; Zou, Jun \n9129 NaN Sun, Qiming; Zhang, Xing; Banerjee, Samragni; ... \n24590 NaN Jiang, Xiaohui; Zhang, Weiyi; Fernie, Alisdair... \n... ... ... \n30650 NaN Guan, Zhitao; Zhang, Yue; Si, Guanlin; Zhou, Z... \n31337 NaN Liu, Hong; Zhang, Yan; Yang, Tao \n4273 NaN Zhou, Yanlai; Chang, Fi-John; Chen, Hua; Li, Hong \n6761 NaN Li, Ya; Tao, Jianhua; Schuller, Bjoern; Shan, ... \n41900 NaN Shang, Ronghua; Kong, Jiarui; Zhang, Weitong; ... \n\n Book Author Full Names Group Authors \n20209 NaN NaN \\\n9308 NaN NaN \n26162 NaN NaN \n9129 NaN NaN \n24590 NaN NaN \n... ... ... \n30650 NaN NaN \n31337 NaN NaN \n4273 NaN NaN \n6761 NaN NaN \n41900 NaN NaN \n\n Article Title \n20209 Interactive POS-aware network for aspect-level... \\\n9308 Uncertainty-Aware Contact-Safe Model-Based Rei... \n26162 An analysis of stochastic variance reduced gra... \n9129 Recent developments in the PySCF program package \n24590 Combining novel technologies with interdiscipl... \n... ... \n30650 ECOSECURITY: Tackling Challenges Related to Da... \n31337 Blockchain-Enabled Security in Electric Vehicl... \n4273 Exploring Copula-based Bayesian Model Averagin... \n6761 MEC 2016: The Multimodal Emotion Recognition C... \n41900 Uncorrelated feature selection via sparse late... \n\n Source Title ... \n20209 NEUROCOMPUTING ... \\\n9308 IEEE ROBOTICS AND AUTOMATION LETTERS ... \n26162 INVERSE PROBLEMS ... \n9129 JOURNAL OF CHEMICAL PHYSICS ... \n24590 PLANT JOURNAL ... \n... ... ... \n30650 IEEE CONSUMER ELECTRONICS MAGAZINE ... \n31337 IEEE NETWORK ... \n4273 JOURNAL OF CLEANER PRODUCTION ... \n6761 PATTERN RECOGNITION (CCPR 2016), PT II ... \n41900 PATTERN RECOGNITION ... \n\n WoS Categories \n20209 Computer Science, Artificial Intelligence \\\n9308 Robotics \n26162 Mathematics, Applied; Physics, Mathematical \n9129 Chemistry, Physical; Physics, Atomic, Molecula... \n24590 Plant Sciences \n... ... \n30650 Computer Science, Hardware & Architecture; Eng... \n31337 Computer Science, Hardware & Architecture; Com... \n4273 Green & Sustainable Science & Technology; Engi... \n6761 Computer Science, Artificial Intelligence; Com... \n41900 Computer Science, Artificial Intelligence; Eng... \n\n Web of Science Index \n20209 Science Citation Index Expanded (SCI-EXPANDED) \\\n9308 Science Citation Index Expanded (SCI-EXPANDED) \n26162 Science Citation Index Expanded (SCI-EXPANDED) \n9129 Science Citation Index Expanded (SCI-EXPANDED) \n24590 Science Citation Index Expanded (SCI-EXPANDED) \n... ... \n30650 Science Citation Index Expanded (SCI-EXPANDED) \n31337 Science Citation Index Expanded (SCI-EXPANDED) \n4273 Science Citation Index Expanded (SCI-EXPANDED) \n6761 Conference Proceedings Citation Index - Scienc... \n41900 Science Citation Index Expanded (SCI-EXPANDED) \n\n Research Areas IDS Number \n20209 Computer Science PI6QI \\\n9308 Robotics RK6JU \n26162 Mathematics; Physics YB5KT \n9129 Chemistry; Physics MP0IB \n24590 Plant Sciences XW2NZ \n... ... ... \n30650 Computer Science; Engineering; Telecommunications HK7MI \n31337 Computer Science; Engineering; Telecommunications GI4BW \n4273 Science & Technology - Other Topics; Engineeri... 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4273JZhou, YL; Chang, FJ; Chen, H; Li, HNaNNaNNaNZhou, Yanlai; Chang, Fi-John; Chen, Hua; Li, HongNaNNaNExploring Copula-based Bayesian Model Averagin...JOURNAL OF CLEANER PRODUCTION...Green & Sustainable Science & Technology; Engi...Science Citation Index Expanded (SCI-EXPANDED)Science & Technology - Other Topics; Engineeri...LT6HINaNGreen AcceptedNaNNaN2023-04-28WOS:000537169400007
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Publication TypeAuthorsBook AuthorsBook EditorsBook Group AuthorsAuthor Full NamesBook Author Full NamesGroup AuthorsArticle TitleSource Title...WoS CategoriesWeb of Science IndexResearch AreasIDS NumberPubmed IdOpen Access DesignationsHighly Cited StatusHot Paper StatusDate of ExportUT (Unique WOS ID)
29758CFu, YC; Liu, YH; Gao, ZWNaNYu, HNaNFu, Yichuan; Liu, Yuanhong; Gao, ZhiweiNaNNaNMultiple Actuator Fault Classification in Wind...2019 25TH IEEE INTERNATIONAL CONFERENCE ON AUT......Automation & Control Systems; Computer Science...Conference Proceedings Citation Index - Scienc...Automation & Control Systems; Computer ScienceBP9ANNaNNaNNaNNaN2023-04-28WOS:000568623100060
34098JHan, D; Zhang, CH; Fauconnier, MLNaNNaNNaNHan, Dong; Zhang, Chun-Hui; Fauconnier, Marie-...NaNNaNEffect of Seasoning Addition on Volatile Compo...FOODS...Food Science & TechnologyScience Citation Index Expanded (SCI-EXPANDED)Food Science & TechnologyPV8DT33406625.0gold, Green PublishedNaNNaN2023-04-28WOS:000610212800001
55478CXu, YX; Liu, M; Peng, L; Zhang, JQ; Zheng, YWNaNNaNIEEEXu, Yuxuan; Liu, Ming; Peng, Linning; Zhang, J...NaNNaNColluding RF Fingerprint Impersonation Attack ...IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION......TelecommunicationsConference Proceedings Citation Index - Scienc...TelecommunicationsBT9VGNaNNaNNaNNaN2023-04-28WOS:000864709903078
32260CLiu, Q; Cai, WD; Fu, ZJ; Shen, J; Linge, NNaNFang, WC; Vasilakos, T; Stoica, A; Kwak, YSNaNLiu, Qi; Cai, Weidong; Fu, Zhangjie; Shen, Jia...NaNNaNAn Optimized Strategy for Speculative Executio...2015 9TH INTERNATIONAL CONFERENCE ON FUTURE GE......Computer Science, Hardware & ArchitectureConference Proceedings Citation Index - Scienc...Computer ScienceBF1GENaNNaNNaNNaN2023-04-28WOS:000380393500003
8751JShamshirband, S; Nodoushan, EJ; Adolf, JE; Man...NaNNaNNaNShamshirband, Shahaboddin; Nodoushan, Ehsan Ja...NaNNaNEnsemble models with uncertainty analysis for ...ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUI......Engineering, Multidisciplinary; Engineering, M...Science Citation Index Expanded (SCI-EXPANDED)Engineering; MechanicsHE2WUNaNGreen Published, goldYN2023-04-28WOS:000453212200001
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6151CSeufert, M; Casas, P; Wehner, N; Gang, L; Li, KNaNGalis, A; Guillemin, F; Noldus, R; Secci, S; I...NaNSeufert, Michael; Casas, Pedro; Wehner, Nikola...NaNNaNStream-based Machine Learning for Real-time Qo...PROCEEDINGS OF THE 2019 22ND CONFERENCE ON INN......Computer Science, Hardware & Architecture; Com...Conference Proceedings Citation Index - Scienc...Computer ScienceBM8PPNaNNaNNaNNaN2023-04-28WOS:000469794500014
32052JHuber, A; Kinna, D; Huber, V; Arnoux, G; Balbo...NaNNaNNaNHuber, A.; Kinna, D.; Huber, V.; Arnoux, G.; B...NaNJET ContributorsThe near infrared imaging system for the real-...PHYSICA SCRIPTA...Physics, MultidisciplinaryScience Citation Index Expanded (SCI-EXPANDED)...PhysicsFL3JXNaNNaNNaNNaN2023-04-28WOS:000414120500027
27985JDong, GP; Ma, J; Kwan, MP; Wang, YM; Chai, YWNaNNaNNaNDong, Guanpeng; Ma, Jing; Kwan, Mei-Po; Wang, ...NaNNaNMulti-level temporal autoregressive modelling ...INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMAT......Computer Science, Information Systems; Geograp...Science Citation Index Expanded (SCI-EXPANDED)...Computer Science; Geography; Physical Geograph...GS7LKNaNhybrid, Green PublishedNaNNaN2023-04-28WOS:000443882300004
2939JYin, ZY; Jin, YF; Huang, HW; Shen, SLNaNNaNNaNYin Zhen-Yu; Jin Yin-Fu; Huang Hong-Wei; Shen ...NaNNaNEvolutionary polynomial regression based model...ENGINEERING GEOLOGY...Engineering, Geological; Geosciences, Multidis...Science Citation Index Expanded (SCI-EXPANDED)Engineering; GeologyDS2IGNaNNaNNaNNaN2023-04-28WOS:000380592100015
34651JWang, JH; Lindenbergh, R; Menenti, MNaNNaNNaNWang, Jinhu; Lindenbergh, Roderik; Menenti, Ma...NaNNaNSigVox - A 3D feature matching algorithm for a...ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SEN......Geography, Physical; Geosciences, Multidiscipl...Science Citation Index Expanded (SCI-EXPANDED)Physical Geography; Geology; Remote Sensing; I...EX2BVNaNNaNNaNNaN2023-04-28WOS:000403031400010
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" }, "execution_count": 4, "metadata": {}, @@ -71,17 +71,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of initial (valid interval) records: 56196\n", - "Number of METRIX filtered records: 49854\n", - "Number of unindexed records: 2984\n", - "Number of filtered records (dropping duplicates): 49839\n" + "Number of initial (valid interval) records: 56196\n" ] + }, + { + "data": { + "text/plain": " Domain_English Field_English \n0 Applied Sciences Agriculture, Fisheries & Forestry \\\n1 Applied Sciences Agriculture, Fisheries & Forestry \n2 Applied Sciences Agriculture, Fisheries & Forestry \n3 Applied Sciences Agriculture, Fisheries & Forestry \n4 Applied Sciences Agriculture, Fisheries & Forestry \n\n SubField_English 2.00 SEQ Source_title srcid \n0 Agronomy & Agriculture 1 Annals of Biology 13016 \\\n1 Agronomy & Agriculture 1 Advances in Agronomy 14324 \n2 Agronomy & Agriculture 1 European Journal of Soil Biology 14648 \n3 Agronomy & Agriculture 1 Soil Biology and Biochemistry 14802 \n4 Agronomy & Agriculture 1 Agricultura Tecnica 14972 \n\n issn_type issn \n0 issn1 09700153 \n1 issn1 00652113 \n2 issn1 11645563 \n3 issn1 00380717 \n4 issn1 03652807 ", + "text/html": "
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" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -95,6 +101,48 @@ "metrix = metrix.set_index([c for c in metrix.columns if \"issn\" not in c]).stack().reset_index()\n", "metrix = metrix.rename(columns={'level_6':\"issn_type\", 0:\"issn\"})\n", "metrix[\"issn\"]=metrix[\"issn\"].str.replace(\"-\",\"\").str.lower().str.strip()\n", + "metrix.head()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "Domain_English 6\nField_English 21\nSubField_English 175\ndtype: int64" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrix[[\"Domain_English\",\"Field_English\",\"SubField_English\"]].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of initial (valid interval) records: 56196\n", + "Number of METRIX filtered records: 49854\n", + "Number of unindexed records: 2984\n", + "Number of filtered records (dropping duplicates): 49839\n" + ] + } + ], + "source": [ "\n", "wos[\"issn\"] = wos[\"ISSN\"].str.replace(\"-\",\"\").str.lower().str.strip()\n", "wos[\"eissn\"] = wos[\"eISSN\"].str.replace(\"-\",\"\").str.lower().str.strip()\n",